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Matplotlib VS Recoll

Compare Matplotlib VS Recoll and see what are their differences

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Matplotlib logo Matplotlib

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

Recoll logo Recoll

Recoll is a desktop full-text search tool. Recoll finds keywords inside documents as well as file names.
  • Matplotlib Landing page
    Landing page //
    2023-06-14
  • Recoll Landing page
    Landing page //
    2023-09-21

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.

Recoll features and specs

  • Comprehensive Indexing
    Recoll can index a wide variety of file types and contents, including emails, documents, and multimedia files. This makes it highly versatile for different data indexing needs.
  • Highly Customizable
    Users can tweak Recoll to meet their specific needs, from defining indexing rules to configuring the GUI settings. This level of customization allows for personalized usage.
  • Powerful Search Capabilities
    It provides advanced search features such as Boolean searches, phrase searches, and filtering by file type or date, which help users find exactly what they are looking for quickly.
  • Cross-Platform Availability
    Recoll is available on multiple operating systems including Linux, Windows, and macOS, making it accessible to a wide range of users.
  • Open Source
    Being open-source, it allows users to view the source code and contribute to its development. It also means there are no licensing fees associated with its use.
  • Support for Multiple Languages
    Recoll supports multiple languages, which makes it a suitable choice for international users.

Possible disadvantages of Recoll

  • Resource Intensive
    The indexing process can be resource-intensive, consuming significant CPU and memory, particularly when handling large datasets.
  • Complex Setup
    Initial setup and configuration can be complex and may require a good understanding of its settings and features, which may not be user-friendly for beginners.
  • User Interface
    While functional, the user interface is considered less modern and may not be as intuitive or visually appealing as some commercial alternatives.
  • Limited Customer Support
    As an open-source project, customer support is primarily community-driven, which may not be as reliable or fast as professional support services.
  • Frequent Updates
    While frequent updates can be beneficial, they may also require users to frequently update their installations and adapt to changes, which can be inconvenient.
  • Limited Mobile Support
    Recoll has limited support for mobile platforms, which may be an important consideration for users who need cross-platform, mobile-friendly access.

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 Recoll

Overall verdict

  • Recoll is generally considered a good tool for those in need of an efficient and reliable desktop search solution. Its combination of features, ease of use, and effectiveness in searching a diverse array of document types make it a commendable choice.

Why this product is good

  • Recoll is a powerful desktop search tool that indexes a wide variety of file formats and provides fast searching capabilities. It is appreciated for its comprehensive indexing, including full-text search and support for advanced queries. Users often highlight its ability to handle complex searches with precision and its user-friendly interface. Additionally, it supports a wide range of document types, which makes it versatile for varied use cases.

Recommended for

    Recoll is recommended for individuals or professionals who frequently need to search through a large number of documents on their computer. It is especially useful for researchers, students, or office workers who deal with a wide range of file types and need quick access to specific information within those files.

Matplotlib videos

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Recoll videos

DEF CON 25 Recon Village - Dakota Nelson -Total Recoll

More videos:

  • Review - Tutorial RECOLL
  • Review - Ubuntu Total "Recoll" - HDD Volltextsuche

Category Popularity

0-100% (relative to Matplotlib and Recoll)
Data Science And Machine Learning
File Manager
0 0%
100% 100
Technical Computing
100 100%
0% 0
Note Taking
0 0%
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 Recoll

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.
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.
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.
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...

Recoll Reviews

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Social recommendations and mentions

Based on our record, Matplotlib seems to be more popular. It has been mentiond 114 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.

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 / 8 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
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Recoll mentions (0)

We have not tracked any mentions of Recoll yet. Tracking of Recoll recommendations started around Mar 2021.

What are some alternatives?

When comparing Matplotlib and Recoll, 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.

DocFetcher - DocFetcher is a portable German/English open source desktop search application.

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

Everything by Voidtools - Everything. Locate files and folders by name instantly. Everything. Small installation file. Clean and simple user interface.

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

SearchMyFiles - Alternative to the standard Search For Files And Folders module of Windows. Duplicates search is also supported.