Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
ZingChart
Highcharts
AnyChart
D3.js
Chart.js
ZoomCharts
Google Charts
CanvasJS
A pioneer in the world of data visualization, ZingChart is a powerful JavaScript library built with big data in mind. With more than 50 chart types and easy integration with your development stack, ZingChart allows you to create interactive and responsive charts with ease.
Matplotlib ZingChartZingChart is recommended for developers, data analysts, and businesses that require dynamic and responsive data visualization capabilities in their web applications. It is particularly well-suited for projects involving large datasets, real-time updates, or complex interactive visualizations.
Straightforward JSON configuration, documentation & demos make it easy to get started with ZingChart without too much initial overhead, even for entry-level devs. For example, here's how to build an animated line chart in a minute.
For those looking for more advanced features, ZingChart's API lets devs create interactions, leverage and interact with the chart autonomously, and allows for the extension of chart types. There are quite a few API demos available upon which to base new interactivity or functionality.
Full disclosure: I work on the ZingSoft team, which includes ZingChart and ZingGrid ๐๐ฝ
Based on our record, Matplotlib seems to be more popular. It has been mentiond 114 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.
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
Numbers are useful, but sometimes itโs 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
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
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 10 months ago
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, Aโฆ. - Source: dev.to / 11 months ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Highcharts - A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application
NumPy - NumPy is the fundamental package for scientific computing with Python
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.
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
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.
