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Google Charts VS Scikit-learn

Compare Google Charts VS Scikit-learn and see what are their differences

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Google Charts logo Google Charts

Interactive charts for browsers and mobile devices.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Google Charts Landing page
    Landing page //
    2023-05-10
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Google Charts features and specs

  • Easy Integration
    Google Charts can be easily integrated with web applications by adding a simple script tag and using JavaScript for customization.
  • Wide Variety of Chart Types
    Google Charts supports a wide range of chart types including line charts, bar charts, pie charts, and more, allowing for comprehensive data visualization.
  • Dynamic Data Handling
    The library allows for dynamic data handling and real-time updates, enabling interactive and responsive charts.
  • Cross-Browser Compatibility
    Google Charts is compatible with most modern browsers, ensuring a consistent experience across different platforms.
  • Customizable
    Offers extensive customization options such as modifying colors, labels, and tooltips, which allows developers to tailor visualizations to their specific needs.
  • Free to Use
    Google Charts is free to use, making it an appealing choice for developers looking for cost-effective data visualization solutions.
  • Comprehensive Documentation
    Provides extensive documentation and tutorials, which helps developers to quickly get started and resolve issues efficiently.

Possible disadvantages of Google Charts

  • Dependency on Google
    Requires an internet connection to fetch the Google Charts library, and performance can be affected if there are connectivity issues.
  • Limited Customization Compared to Alternatives
    Though customizable, it has fewer options and flexibility compared to other libraries like D3.js, which might be a limitation for advanced users.
  • Load Time
    The initial loading time of Google Charts can be slower compared to lightweight charting libraries due to the need to retrieve data from Google's servers.
  • Security Concerns
    As it relies on loading scripts from Google's servers, there might be security concerns in highly sensitive applications.
  • Not Open Source
    Google Charts is not open source, which might be a barrier for developers who prefer open-source solutions for greater control and transparency.
  • Limited Offline Support
    Static charts cannot be easily generated without an internet connection, limiting its use in offline applications.

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Google Charts videos

+ Add

Data Visualization for the Web Using Google Charts

More videos:

  • Review - Incorporating Google Charts in a FileMaker Solution | FileMaker Training
  • Review - Google Charts for Native Android Apps

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Category Popularity

0-100% (relative to Google Charts and Scikit-learn)

Google Charts

Scikit-learn

Data Dashboard
74 74%
Data Dashboard
26% 26
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Visualization
100 100%
Data Visualization
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Google Charts and Scikit-learn. For example, how are they different and which one is better?
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Reviews

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

Google Charts Reviews

15 JavaScript Libraries for Creating Beautiful Charts
Google Charts also comes with various customization options that help in changing the look of the graph. Charts are rendered using HTML5/SVG to provide cross-browser compatibility and cross-platform portability to iPhones, iPads, and Android. It also includes VML for supporting older IE versions.
Source: www.sitepoint.com
Top 10 JavaScript Charting Libraries for Every Data Visualization Need
Google Charts is an excellent choice for projects that do not require complicated customization and prefer simplicity and stability.
Source: hackernoon.com
A Complete Overview of the Best Data Visualization Tools
Google Charts is a powerful, free data visualization tool that is specifically for creating interactive charts for embedding online. It works with dynamic data and the outputs are based purely on HTML5 and SVG, so they work in browsers without the use of additional plugins. Data sources include Google Spreadsheets, Google Fusion Tables, Salesforce, and other SQL databases.
Source: www.toptal.com
The Best Data Visualization Tools - Top 30 BI Software
Google Charts runs on SVG and HTML5, aiming for Android, iOS and total cross-browser compatibility, including older versions of Internet Explorer. All of the charts you can create are interactive and you may be able zoom in on some of them. The site offers a fairly comprehensive gallery where you can find a variety of types of visualizations and interactions that you can use.
Source: improvado.io

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Google Charts. It has been mentiond 31 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.

Google Charts mentions (10)

  • The top 11 React chart libraries for data visualization
    This library leverages the robustness of Google’s chart tools combined with a React-friendly experience. It is ideal for developers familiar with Google’s visualization ecosystem. - Source: dev.to / over 1 year ago
  • Using Images in a chart?
    I tried adding the images as labels and it didn't work. If this is possible at all, it would probably require Google Charts. Source: about 2 years ago
  • What are some good graph visualization libraries?
    Google's is a bit simpler to work with but more basic in terms of features https://developers.google.com/chart. Source: over 2 years ago
  • 5 Best Free JS Chart Libraries
    Google charts Https://developers.google.com/chart. - Source: dev.to / over 2 years ago
  • Suggestions for super simple QR code generator
    I did find a nice solution for Access forms where you can use a web browser control and developers.google.com/chart to render a QR code in that control based on the contents of other controls (textboxes, comboboxes, etc.,.). This would be perfect if it didn't a) rely on an active WAN connection and b) rely on that specific URL being active indefinitely. Source: almost 3 years ago
View more

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
View more

What are some alternatives?

When comparing Google Charts and Scikit-learn, you can also consider the following products

Highcharts - A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application

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

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

OpenCV - OpenCV is the world's biggest computer vision library

Chart.js - Easy, object oriented client side graphs for designers and developers.

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

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