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Matplotlib VS ParseHub

Compare Matplotlib VS ParseHub and see what are their differences

HasData
HasData is a top web scraping platform for developers and enterprises. It delivers structured, real-time data from the web using scalable APIs and no-code tools, removing the need to manage proxies, browsers, or anti-bot systems. featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Matplotlib logo Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...

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.
  • Matplotlib Landing page
    Landing page //
    2023-06-14
  • ParseHub Landing page
    Landing page //
    2021-09-12

Matplotlib features and specs

  • Versatility
    Matplotlib can generate a wide variety of plots, ranging from simple line plots to complex 3D plots. This versatility makes it a go-to library for many scientific and technical visualizations.
  • Customization
    It offers extensive customization options for virtually every element of a plot, including colors, labels, line styles, and more, allowing users to tailor plots to meet specific needs.
  • Integrations
    Matplotlib integrates well with other Python libraries such as NumPy, Pandas, and SciPy, making it easier to plot data directly from these sources.
  • Community and Documentation
    It has a large, active community and comprehensive documentation that includes tutorials, examples, and detailed references, which can help users solve problems and improve their plot-making skills.
  • Interactivity
    Matplotlib supports interactive plots, which can be embedded in Jupyter notebooks and GUIs, allowing for dynamic data exploration and presentation.
  • Publication-Quality
    The library is capable of producing high-quality, publication-ready graphics that meet the stringent requirements of academic journals and professional presentations.

Possible disadvantages of Matplotlib

  • Complexity
    While Matplotlib offers extensive customization, it can be complex and sometimes unintuitive for beginners, requiring a steep learning curve to master all its functionality.
  • Performance
    Rendering a large number of plots or handling very large datasets can be slow, making Matplotlib less suitable for real-time data visualization.
  • Modern Aesthetics
    Out-of-the-box plots from Matplotlib can look somewhat dated compared to those from newer plotting libraries like Seaborn or Plotly, requiring additional customization to achieve a modern look.
  • 3D Plots
    Although Matplotlib supports 3D plotting, its capabilities are relatively limited and less sophisticated compared to specialized 3D plotting libraries.
  • Size and Structure
    The package is relatively large and can be slow to import. Its extensive structure can make finding specific functions and understanding the overall architecture challenging.

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.

Analysis of Matplotlib

Overall verdict

  • Yes, Matplotlib is a good library for data visualization, particularly for users who require a versatile and powerful plotting solution in Python.

Why this product is good

  • Matplotlib is highly regarded due to its extensive customization options, versatility in creating a wide range of static, animated, and interactive plots, and its large user community and support. It integrates well with other scientific libraries in Python, making it a staple for data visualization. The library is also open-source and frequently updated, ensuring it remains a reliable choice for users.

Recommended for

  • Data scientists and analysts needing to create detailed, customized visual representations of their data.
  • Researchers and engineers looking for a comprehensive plotting library that supports scientific and engineering formats.
  • Python developers who require integration with other scientific computing libraries like NumPy and Pandas.

Analysis of ParseHub

Overall verdict

  • ParseHub is generally a reliable and effective tool for web scraping purposes. Its ease of use and powerful features make it a strong choice for both beginners and experienced data analysts. However, users should be aware of potential limitations regarding speed and handling extremely large-scale data scraping tasks.

Why this product is good

  • ParseHub is considered a good tool due to its versatility in web scraping without requiring extensive programming knowledge. It provides a user-friendly interface that allows users to automate data extraction tasks from websites. Additionally, it supports complex website structures and can handle dynamic content and JavaScript-driven sites.

Recommended for

    ParseHub is recommended for business analysts, data scientists, researchers, and anyone who needs to extract data from websites regularly but does not wish to dive deeply into coding. It's also a good option for individuals or small businesses looking to gather market research, product pricing information, or other competitive intelligence from web sources.

Matplotlib videos

+ Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

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 Matplotlib and ParseHub)

Matplotlib

ParseHub

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Web Scraping
0 0%
Web Scraping
100% 100
Technical Computing
100 100%
Technical Computing
0% 0
Data Extraction
0 0%
Data Extraction
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 Matplotlib and ParseHub

Matplotlib Reviews

25 Python Frameworks to Master
Matplotlib is a widely used tool for data visualization in Python. It provides an object-oriented API for embedding plots into applications.
Source: kinsta.com
5 Best Python Libraries For Data Visualization in 2023
You can use this library for multiple purposes such as generating plots, bar charts, histograms, power spectra, stemplots, pie charts, and more. The best thing about Matplotlib is you just have to write a few lines of code and it handles the rest by itself. Metaplotilib focuses on static images for publication along with interactive figures using toolkits like Qt and GTK.
Source: www.technotification.com
15 data science tools to consider using in 2021
Matplotlib is an open source Python plotting library that's used to read, import and visualize data in analytics applications. Data scientists and other users can create static, animated and interactive data visualizations with Matplotlib, using it in Python scripts, the Python and IPython shells, Jupyter Notebook, web application servers and various GUI toolkits.
Source: searchbusinessanalytics.techtarget.com
Top Python Libraries For Image Processing In 2021
Matplotlib is primarily used for 2D visualizations such as scatter plots, bar graphs, histograms, and many more, but we can also use it for image processing. It is effective to get information out of an image. It doesnโ€™t support all file formats.
Source: www.analyticsvidhya.com
Top 8 Python Libraries for Data Visualization
Matplotlib is a data visualization library and 2-D plotting library of Python It was initially released in 2003 and it is the most popular and widely-used plotting library in the Python community. It comes with an interactive environment across multiple platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application...
Source: www.geeksforgeeks.org

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, Matplotlib seems to be a lot more popular than ParseHub. While we know about 114 links to Matplotlib, 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.

Matplotlib mentions (114)

  • The soul file
    In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ€” the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 4 months ago
  • How to Analyze CSV Files with Python and Pandas
    Numbers are useful, but sometimes itโ€™s easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 7 months ago
  • libmalloc, jemalloc, tcmalloc, mimalloc - Exploring Different Memory Allocators
    We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 8 months ago
  • Building an AI Scoring Agent: Step-By-Step
    NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 9 months ago
  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโ€ฆ. - Source: dev.to / 10 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 4 years ago
  • Free for dev - list of software (SaaS, PaaS, IaaS, etc.)
    Parsehub.com โ€” Extract data from dynamic sites, turn dynamic websites into APIs, 5 projects free. - Source: dev.to / almost 5 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 5 years ago

What are some alternatives?

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

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

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.

NumPy - NumPy is the fundamental package for scientific computing with Python

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

Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.

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

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