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Xively VS Matplotlib

Compare Xively VS Matplotlib and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Xively logo Xively

Xively offers an Internet of Things product relationship management solution for enterprises.

Matplotlib logo Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...
  • Xively Landing page
    Landing page //
    2023-09-16
  • Matplotlib Landing page
    Landing page //
    2023-06-14

Xively features and specs

  • Ease of Integration
    Xively offers a simple and straightforward API that makes it easy for developers to integrate IoT devices.
  • Scalability
    The platform is designed to handle a large number of devices and data points, which makes it suitable for growing IoT deployments.
  • Real-time Data
    Xively provides real-time data streaming and analytics, allowing users to make immediate decisions based on live data.
  • Security
    Strong emphasis on security features like device authentication and data encryption to protect sensitive information.
  • User-Friendly Interface
    The platform includes a user-friendly dashboard that helps in monitoring and managing connected devices efficiently.

Possible disadvantages of Xively

  • Cost
    The pricing model can be expensive for small businesses or individual developers compared to some competitors.
  • Limited Customization
    While the platform is user-friendly, it may offer limited customizability for very specific use cases or unique requirements.
  • Dependency on External Service
    Relying on Xively means any downtime or service interruption on their end could affect your IoT deployments.
  • Learning Curve
    Despite its ease of integration, new users may face a learning curve to familiarize themselves with all features and capabilities.
  • Data Storage Limitations
    There might be limitations on data storage, which could be a concern for applications requiring extensive historical data analysis.

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.

Analysis of Xively

Overall verdict

  • As of February 2018, Xively was acquired by Google and its services were integrated into Google's cloud platform offerings, specifically into the Google Cloud IoT Core. Therefore, Xively as a standalone platform does not exist anymore. Companies seeking similar services should explore Google's IoT offerings.

Why this product is good

  • Xively, a former IoT platform, was known for its ability to connect devices and integrate data for analytics and monitoring. It provided robust tools for device management and offered a comprehensive suite of APIs to streamline IoT deployments. However, the platform's reputation varied depending on users' company size, budget, and technical needs. Its strength lay in its ease of integration and focus on building IoT solutions, which was beneficial for companies looking to improve their IoT infrastructure rapidly.

Recommended for

    Google Cloud IoT Core, the service that absorbed Xively, is recommended for enterprises looking for large-scale IoT solutions that require robust cloud integration capabilities, particularly those already using or planning to use Google Cloud services.

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.

Xively videos

+ Add

Internet of Things with Xively, Arduino and the CC3000 WiFi chip

More videos:

  • Review - Cloud Laser Doorbell with Xively

Matplotlib videos

+ Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Category Popularity

0-100% (relative to Xively and Matplotlib)

Xively

Matplotlib

IoT Platform
100 100%
IoT Platform
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Dashboard
65 65%
Data Dashboard
35% 35
Technical Computing
0 0%
Technical Computing
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 Xively and Matplotlib

Xively Reviews

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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

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.

Xively mentions (0)

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

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
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What are some alternatives?

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

Losant - Losant makes building connected experiences and solutions easy.

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

Hologram.io - Cellular IoT connectivity that powers innovation

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

Cisco Jasper - Jasper provides a SaaS IoT platform to enable companies of all sizes to launch, manage and monetize IoT services on a global scale.

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

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