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Steam Database VS Matplotlib

Compare Steam Database 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

Steam Database logo Steam Database

This tool was made to give better insight into the applications that Steam has in its absolutely huge database.

Matplotlib logo Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...
  • Steam Database Landing page
    Landing page //
    2019-11-20
  • Matplotlib Landing page
    Landing page //
    2023-06-14

Steam Database features and specs

  • Comprehensive Data
    Steam Database offers detailed information about games, including price history, player counts, and update history, making it a valuable resource for gamers and developers.
  • User-Friendly Interface
    The website features a clean and intuitive layout, making it easy for users to find and understand the data they are looking for.
  • Free Access
    Users can access a wealth of information without any subscription fee, making it accessible to a wide audience.
  • Advanced Search and Filters
    The platform provides robust search and filtering options, allowing users to easily narrow down results based on various criteria such as genre, release date, and rating.
  • Sale Alerts and Notifications
    Users can set up notifications for price drops and sales, ensuring they never miss a deal on their favorite games.
  • API Access
    For developers, Steam Database offers an API that allows programmatic access to its rich dataset, enabling integration with other applications.
  • Community Tools
    The site includes additional tools and features like package comparisons and depots, which are useful for both casual users and industry professionals.

Possible disadvantages of Steam Database

  • Unofficial Source
    Since Steam Database is not officially affiliated with Valve Corporation, the data reliability and accuracy might occasionally be questioned.
  • Data Overload
    The sheer volume of data available can be overwhelming for casual users who may not be familiar with how to interpret it.
  • Limited Mobile Support
    While the desktop experience is robust, the mobile interface may not be as optimized, potentially leading to a less satisfactory user experience on smartphones and tablets.
  • Potential for Outdated Information
    Due to the nature of tracking a large volume of data, there may occasionally be delays in updating information, leading to temporary inaccuracies.
  • Complex Features
    Some advanced features and data points may be difficult to understand for users without a technical background or industry knowledge.

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 Steam Database

Overall verdict

  • Yes, Steam Database is a good tool for those interested in detailed metrics and statistics related to games on the Steam platform. Itโ€™s well-regarded for its accuracy and depth of information.

Why this product is good

  • Steam Database is considered a valuable resource for gamers and developers because it provides comprehensive information about games available on the Steam platform. This includes changes in game prices, player statistics, app and package details, and historical data. Itโ€™s particularly useful for tracking price history and sales information, which can help users make informed purchasing decisions. Moreover, developers use SteamDB to monitor user feedback and game performance statistics.

Recommended for

  • Gamers looking to track game sales and price history
  • Developers wanting insights into game performance and user engagement
  • Market analysts researching trends in the gaming industry
  • Enthusiasts interested in detailed Steam app and package 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.

Steam Database videos

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Matplotlib videos

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Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Category Popularity

0-100% (relative to Steam Database and Matplotlib)

Steam Database

Matplotlib

Games
100 100%
Games
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Comparison
100 100%
Comparison
0% 0
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 Steam Database and Matplotlib

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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, Steam Database should be more popular than Matplotlib. It has been mentiond 682 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.

Steam Database mentions (682)

  • Getting a Cease and Desist from Waffle House
    I believe scraping is generally ok - there's actual trademark law about trademarks, which is why you got a c+d about trademark usage, instead of a general 'stop what you're doing we don't like it' c+d. A good point of comparison is steam db (and other similar sites), which uses Steam public info to triangulate market info that isn't immediately apparent. https://steamdb.info/. - Source: Hacker News / about 1 year ago
  • Show HN: I scrape Steam data every month and it's yours to download for free
    Do you have data that https://steamdb.info/ doesnโ€™t have? - Source: Hacker News / over 1 year ago
  • What are the chance that baldur gates 3 become free in steam DB ?
    Asking if you should buy a game now or wait for a sale isn't allowed, asking when a game will go on sale is not allowed, asking how big of a discount a game might get is not allowed. Use SteamDB to look at sale histories on games. Source: over 2 years ago
  • A big chunk of my wishlist just went on sale
    Here's how to cure you from your buying habit, checkout https://steamdb.info/, check the price history of the game you're thinking of getting. Most likely it's on sale once every odd month, and discount percentages are only ever increasing over time. So really you can just buy it when you think you have time to play it soon. Source: over 2 years ago
  • The finals is trending on steam
    Correct, it's trending on https://steamdb.info/ if you look at the panel, some games will show zero players. But OP is wrong, other games are trending too. Source: over 2 years ago
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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 / 5 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 / 9 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 / 10 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 / 11 months ago
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What are some alternatives?

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

IsThereAnyDeal - "When the price is right, you will play all night."

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

GG.DEALS - Very good and clear site for best deals.

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

Steam Charts - An ongoing analysis of Steam's concurrent players.

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

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