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NumPy VS AnyChart

Compare NumPy VS AnyChart and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

AnyChart logo AnyChart

Award-winning JavaScript charting library & Qlik Sense extensions from a global leader in data visualization! Loved by thousands of happy customers, including over 75% of Fortune 500 companies & over half of the top 1000 software vendors worldwide.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • AnyChart Home Page of AnyChart JS Charts
    Home Page of AnyChart JS Charts //
    2025-03-10

Founded in 2003, AnyChart is one of the global leaders in interactive data visualization, offering award-winning, flexible JavaScript (HTML5) charting libraries with numerous chart types and features, great API & documentation, and enterprise-grade support.

Cross-browser JS charts and graphs, maps, stock charts, and Gantt charts powered by AnyChart have helped thousands of companies including industry leaders โ€” from startups to corporate giants such as AT&T, Bosch, BP, Citi, ExxonMobil, Lockheed Martin, Merck, Novartis, Oracle, Reuters, Samsung, Tencent, UBS, Volkswagen, Yahoo, 3M & many others โ€” gain better insight, make right decisions, and improve their enterprise performance based on robust, insightful data visualization.

Whether you need to enhance your website with better reporting, embed dashboards into your on-premises and SaaS systems, or build an entirely new product, AnyChart covers all your data visualization needs. The company's products include massive out-of-the-box capabilities, combined with flexibility & simplicity.

Loved by thousands of happy customers, including more than 75% of Fortune 500 companies across all industries and over half of the top 1,000 software vendors worldwide.

In 2019, AnyChart launched a technology alliance partnership with Qlik, adding three new product extensions for Qlik Sense. The partnership enables the Qlik community to be provided with more than 30 new chart types and many valuable features natively in the Qlik environment.

AnyChart

$ Details
freemium $49.0 / One-off (Next Unicorn license for startups)
Platforms
JavaScript Web Qlik Windows Mac OSX Linux Android iOS TypeScript PHP Google Chrome Safari Opera Firefox Java iPhone Mobile Laravel ReactJS React Native Angular Python Node JS Cross Platform
Release Date
2003 May
Startup details
Country
United States
State
Florida
Founder(s)
Anton Baranchuk
Employees
10 - 19

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.

AnyChart features and specs

  • Chart types
    70+ (bar, line, Gantt, candlestick, waterfall, sunburst...)
  • Data formats
    Multiple (JavaScript API, XML, JSON, CSV, HTML table, Google Sheets...)
  • Integrations
    Seamlessly runs with any language, framework, and database (multiple integration templates are available)
  • Docs
    The documentation and API reference are very detailed and everything is explained in detail in a simple and clear way, with numerous readymade chart samples
  • Browser support
    Supports all browsers, including IE6+ along with mobile browsers
  • Dependencies
    None
  • Product history
    AnyChart has been operating from 2003 and the team is very experienced with a long history of releasing high-quality products.
  • Open source
    The open source code is hosted on GitHub under different licenses depending on the library
  • Flexibility
    Extremely flexible and customizable Any part of a chart can be changed and customized.
  • Interactivity
    Events can be distributed to chart elements which respond to user actions. Event listeners are simple JavaScript functions which are very easy to use and understand

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

NumPy videos

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

AnyChart videos

Heatmap Chart using AnyChart with Python

More videos:

  • Tutorial - Creating Interactive Charts with AnyChart library for Your Android App
  • Tutorial - How to Create a Gantt Chart in Qlik Sense using AnyGantt Extension by AnyChart

Category Popularity

0-100% (relative to NumPy and AnyChart)
Data Science And Machine Learning
Data Dashboard
28 28%
72% 72
Data Science Tools
100 100%
0% 0
Charting Libraries
0 0%
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 NumPy and AnyChart

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.
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

AnyChart Reviews

  1. alairedeforest
    Fast, effective charts

    Probably the best JS chart library on the market right now.

    ๐Ÿ Competitors: CanvasJS
    ๐Ÿ‘ Pros:    Extremely simple|Fast|Affordable
    ๐Ÿ‘Ž Cons:    Not free

15 JavaScript Libraries for Creating Beautiful Charts
AnyChart is a lightweight and robust JavaScript charting library with charts designed to be embedded and integrated. AnyChart allows you to display 68 charts out-of-the-box and provides features to create your own chart types. You can save a chart as an image in PDF, PNG, JPG or SVG format.
Top 10 Visual Analytics Provider For 2021
AnyChart provides products for those who are slightly well-versed with HTML and JavaScript. Their products provide robust JavaScript charting libraries with APIs, documentation, and enterprise-grade support. Developers can integrate a variety of charts into their mobile, desktops, or web products. Their component is compatible with any database and runs on any platform....
Top 10 JavaScript Charting Libraries for Every Data Visualization Need
AnyChart is a robust, lightweight and feature-rich JS chart library with rendering in SVG/VML. It actually gives web developers a great opportunity to create any different charts that will help to make decisions based on what is seen.
Source: hackernoon.com

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 122 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.

NumPy mentions (122)

View more

AnyChart mentions (0)

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

What are some alternatives?

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

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.

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

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

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.