Software Alternatives, Accelerators & Startups
Register | Login

Steam Charts VS Matplotlib

Compare Steam Charts VS Matplotlib and see what are their differences

VisualVisitor
Consent-Based Identification of the Person Visiting Your Website Including First Name, Last Name, Email & 37 Other Data Points. Identify and Influence Your Engaged Website Visitors into Sales-Ready Leads โ€“ Before You Commit a Single Working Hour. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Steam Charts logo Steam Charts

An ongoing analysis of Steam's concurrent players.

Matplotlib logo Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...
  • Steam Charts Landing page
    Landing page //
    2023-08-18
  • Matplotlib Landing page
    Landing page //
    2023-06-14

Steam Charts features and specs

  • Real-Time Data
    Steam Charts provides real-time data on game player counts and trends, allowing users to track the current popularity of games.
  • Historical Data
    Users can access historical data to see how a game's player base has evolved over time, which can be useful for analyzing trends and seasonal effects.
  • Comparison Tool
    Steam Charts allows users to compare the performance of different games side-by-side, facilitating market research and game development decisions.
  • Visualization
    The platform provides easy-to-understand visual graphs and charts, making complex data accessible even to users without a technical background.
  • Community Insights
    User comments and insights on Steam Charts can provide additional context and background for the quantitative data displayed.

Possible disadvantages of Steam Charts

  • Limited Scope
    Steam Charts only provides data for games available on the Steam platform, excluding games from other platforms such as Epic Games Store, GOG, or console-exclusive titles.
  • Data Reliability
    The accuracy of the data can sometimes be questionable due to potential discrepancies between what Steam reports and actual player activity or server issues.
  • Overemphasis on Quantitative Data
    While visual and numerical data can be insightful, they might not fully capture qualitative factors such as player satisfaction, game quality, or community engagement.
  • No Revenue Data
    Steam Charts does not provide information on game sales or revenue, which are crucial for a more comprehensive understanding of a game's performance.
  • Privacy Concerns
    Sharing user-player data, even in aggregate form, can raise privacy concerns, especially if data is not fully anonymized or if it can be reverse-engineered.

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 Charts

Overall verdict

  • Steam Charts is a valuable tool for individuals interested in analyzing the performance and popularity of Steam games. It is generally regarded as reliable for getting an overview of player activity and trends.

Why this product is good

  • Steam Charts provides real-time and historical data on active players for games on Steam. It is useful for gamers and developers who want to monitor the popularity and player trends of specific games, track engagement over time, and compare concurrent users across different titles.

Recommended for

  • Gamers who want to track the popularity of their favorite games.
  • Game developers interested in analyzing player retention and engagement.
  • Market analysts researching gaming trends.
  • Content creators looking for data to support gaming-related content.

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 Charts videos

No Steam Charts videos yet. You could help us improve this page by suggesting one.

Add video

Matplotlib videos

+ Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Category Popularity

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

Steam Charts

Matplotlib

Games
100 100%
Games
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Business & Commerce
100 100%
Business & Commerce
0% 0
Technical Computing
0 0%
Technical Computing
100% 100

User comments

Share your experience with using Steam Charts and Matplotlib. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Steam Charts and Matplotlib

Steam Charts Reviews

We have no reviews of Steam Charts yet.
Be the first one to post

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 Charts should be more popular than Matplotlib. It has been mentiond 254 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 Charts mentions (254)

  • Scientists find ways to boost memory in aging brains
    > no one really wants to deal with problems... it's the job that keeps you fit. Is it though? "it is estimated that the number of Chess players is about 800 million globally." according to https://www.chessjournal.com/how-many-chess-players-are-there/ I've read ~600M for Mahjong, right now nearly few millions on Steam via https://steamcharts.com etc. So I think just with famous games we can see that billions of... - Source: Hacker News / 9 months ago
  • Microsoft announces Copilot+ PCs with built-in AI hardware
    > The vast majority of gamers game on smartphones and tablets with ARM processors. Those are clearly not the gamers I am talking about. There is a massive market out there of games that do not support those platforms. That are only just now scratching the surface with games like Death Stranding releasing on iPhone and Mac. Except for Nintendo the 2 main AAA consoles are x86 based, and I have seen no rumors of that... - Source: Hacker News / about 2 years ago
  • Discussion Thread
    How long before Skyrim overtakes !ping Starfield? Source: almost 3 years ago
  • Team Fortress 2 game has broken its concurrent player count record: 253k+
    Look at the graphs from steamcharts.com and compare it to other games below & above TF2 and tell me those are real numbers. Source: about 3 years ago
  • Petition to beat the shit out of the guy who came up with these POI traps
    You can actually see this clearly reflected on steam charts. https://steamcharts.com/ . Of the top 25 games the only one that is "easy" is Stardew Valley at 24. Everything else is either difficult or PVP (which is very difficult). Source: about 3 years ago
View more

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
View more

What are some alternatives?

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

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

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

Steam Spy - Steam Spy is Steam stats service based on Web API provided by Valve and cool idea of Kyle Orland...

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

Augmented Steam - Enhanced Steam fork by IsThereAnyDeal.

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

Loading...