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Highcharts VS NumPy

Compare Highcharts VS NumPy and see what are their differences

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Contents:
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  3. » Reviews
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Highcharts logo Highcharts

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

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Highcharts Landing page
    Landing page //
    2023-03-16
  • NumPy Landing page
    Landing page //
    2023-05-13

Highcharts features and specs

  • Customization
    Highcharts provides extensive options to customize chart appearance and functionality, allowing for a tailored and specific data visualization experience.
  • Cross-Browser Compatibility
    Highcharts ensures compatibility across a wide range of browsers, making charts accessible to users regardless of their browser preferences.
  • Wide Range of Chart Types
    Offers a broad spectrum of chart types, including line, bar, pie, scatter, and more, catering to various data visualization needs.
  • Interactive Features
    Includes numerous interactive features such as tooltips, zooming, and clickable points, enhancing user engagement with the data.
  • Strong Community and Support
    Has an active community and provides extensive documentation, forums, and professional support options to assist users in overcoming challenges.
  • Performance
    Optimized for high performance, allowing for the rendering of large datasets without significant lag or performance issues.
  • Exporting and Sharing
    Built-in options for exporting charts to various formats (PNG, JPEG, PDF, etc.) and sharing them easily.

Possible disadvantages of Highcharts

  • Cost
    Highcharts is not free for commercial use, which may be a drawback for small businesses or individual developers with limited budgets.
  • Steep Learning Curve
    Despite comprehensive documentation, the abundance of features and customization options can result in a steeper learning curve for new users.
  • Dependency on JavaScript
    As a JavaScript library, Highcharts requires a solid understanding of JavaScript, making it less accessible for developers not familiar with the language.
  • Limited Free Support
    While there is a free support forum, professional support options are paid, which can be a limitation for users needing urgent assistance without extra costs.
  • Mobile Responsiveness
    Although Highcharts provides some support for mobile responsiveness, achieving optimal performance and displays on all device types may require additional customization.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Highcharts videos

+ Add

Angular 2 & HighCharts Quick-Tip: Dynamic Data & Draggable Points (2016)

More videos:

  • Tutorial - How to define the custom colors for Highcharts?
  • Review - Data Visualization HighCharts

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to Highcharts and NumPy)

Highcharts

NumPy

Data Dashboard
79 79%
Data Dashboard
21% 21
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

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Highcharts and NumPy

Highcharts Reviews

6 JavaScript Charting Libraries for Powerful Data Visualizations in 2023
However, you might need to pay for additional packages to get exactly what you’re looking for. The Highcharts Core package includes all the essentials (like line, bar, area, and pie charts) but Maps, Gantt, and Stock chart packages are all extra. In terms of cost, this makes Highcharts somewhat less scalable, depending on the budget available for your project.
Source: embeddable.com
15 JavaScript Libraries for Creating Beautiful Charts
Highcharts is another very popular library for building graphs. It comes loaded with many different types of cool animations that are sufficient to attract many eyeballs to your website. Just like other libraries, Highcharts comes with many pre-built graphs like spline, area, areaspline, column, bar, pie, scatter, etc. The charts are responsive and mobile-ready. Besides,...
Source: www.sitepoint.com
Best Data Visualization Tools
For companies that want to embed interactive visualizations in their online content, look no further than Datawrapper. Highcharts is another great option for embedding interactive content into your sites, though it’s not as easy for non-specialists as Datawrapper.
Source: neilpatel.com
Top 10 JavaScript Charting Libraries for Every Data Visualization Need
Highcharts is one of the most comprehensive and popular JavaScript charting libraries based on HTML5, rendering in SVG/VML. It is lightweight, supports a wide range of diverse chart types, and ensures high performance.
Source: hackernoon.com
The Best Data Visualization Tools - Top 30 BI Software
Highcharts is a battle-tested SVG-based, multi-platform charting library that has been actively developed since 2009. Its JavaScript API integrates easily, and features robust documentation, advanced responsiveness and industry-leading accessibility support. You can add interactive, mobile-optimized charts to your web and mobile projects. Charts are rendered in SVG and a VML...
Source: improvado.io

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 119 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.

Highcharts mentions (0)

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

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 3 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 7 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 8 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing Highcharts and NumPy, you can also consider the following products

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.

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

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

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Google Charts - Interactive charts for browsers and mobile devices.

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

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