Bokeh vs Chartify

Overview

Bokeh

Stacks95

Followers183

Votes12

GitHub Stars20.2K

Forks4.2K

Chartify

Stacks17

Followers25

Votes0

GitHub Stars3.6K

Forks335

Bokeh vs Chartify : What are the differences?

Architecture: Bokeh follows a client-server architecture where the Bokeh server acts as a standalone web application server and allows for real-time updating of plots. Chartify, on the other hand, is primarily designed for static data visualization without the need for a server, providing lightweight and easy-to-use charting functionality.
Flexibility: Bokeh offers a high level of customization and interactivity, allowing users to create complex and interactive visualizations with a wide range of tools and extensions. Chartify, however, is more focused on simplicity and ease of use, offering out-of-the-box chart templates and a limited set of customization options.
Integration: Bokeh seamlessly integrates with Python scientific libraries such as NumPy, Pandas, and SciPy, making it a powerful tool for data analysis and visualization in scientific computing. In contrast, Chartify is designed to work specifically with data from pandas DataFrames, simplifying the data preparation process but limiting its compatibility with other libraries.
Community Support: Bokeh has a large and active community of users and contributors, providing extensive documentation, tutorials, and plugins to support users in creating advanced visualizations. Chartify, being a relatively newer tool, may have less extensive community support and resources available for users.
Development Focus: Bokeh is aimed at users who require advanced data visualization capabilities, such as interactive plots, dashboards, and complex visualizations for presentations or web applications. Chartify, on the other hand, targets users who prioritize quick and easy chart creation for data exploration and visualization in a simple and straightforward manner.
Backend Dependency: Bokeh relies on a JavaScript library called Backbone.js for providing structure to web applications, allowing for more sophisticated client-side interactions. Chartify, on the other hand, has minimal dependencies and is designed to be self-contained and straightforward to use without complex backend requirements.

In Summary, Bokeh and Chartify differ in architecture, flexibility, integration, community support, development focus, and backend dependencies.

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Detailed Comparison

Bokeh	Chartify
Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.	Consistent input data format: Spend less time transforming data to get your charts to work. All plotting functions use a consistent tidy input data format. Smart default styles: Create pretty charts with very little customization required. Flexibility: Chartify is built on top of Bokeh, so if you do need more control you can always fall back on Bokeh's API.
interactive visualization library ; versatile graphics ; open source; https://github.com/bokeh/bokeh	-
Statistics
GitHub Stars 20.2K	GitHub Stars 3.6K
GitHub Forks 4.2K	GitHub Forks 335
Stacks 95	Stacks 17
Followers 183	Followers 25
Votes 12	Votes 0
Pros & Cons
Pros 12 Beautiful Interactive charts in seconds	No community feedback yet
Integrations
Bootstrap Flask NGINX React Django Python Jupyter Tornado Streamlit	Python

What are some alternatives to Bokeh, Chartify ?

D3.js

It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework.

Highcharts

Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.

Plotly.js

It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.

Chart.js

Visualize your data in 6 different ways. Each of them animated, with a load of customisation options and interactivity extensions.

Recharts

Quickly build your charts with decoupled, reusable React components. Built on top of SVG elements with a lightweight dependency on D3 submodules.

ECharts

It is an open source visualization library implemented in JavaScript, runs smoothly on PCs and mobile devices, and is compatible with most current browsers.

ZingChart

The most feature-rich, fully customizable JavaScript charting library available used by start-ups and the Fortune 100 alike.

amCharts

amCharts is an advanced charting library that will suit any data visualization need. Our charting solution include Column, Bar, Line, Area, Step, Step without risers, Smoothed line, Candlestick, OHLC, Pie/Donut, Radar/ Polar, XY/Scatter/Bubble, Bullet, Funnel/Pyramid charts as well as Gauges.

CanvasJS

Lightweight, Beautiful & Responsive Charts that make your dashboards fly even with millions of data points! Self-Hosted, Secure & Scalable charts that render across devices.

AnyChart

AnyChart is a flexible JavaScript (HTML5) based solution that allows you to create interactive and great looking charts. It is a cross-browser and cross-platform charting solution intended for everybody who deals with creation of dashboard, reporting, analytics, statistical, financial or any other data visualization solutions.

Related Comparisons

Bokeh vs Chartify : What are the differences?

Architecture: Bokeh follows a client-server architecture where the Bokeh server acts as a standalone web application server and allows for real-time updating of plots. Chartify, on the other hand, is primarily designed for static data visualization without the need for a server, providing lightweight and easy-to-use charting functionality.
Flexibility: Bokeh offers a high level of customization and interactivity, allowing users to create complex and interactive visualizations with a wide range of tools and extensions. Chartify, however, is more focused on simplicity and ease of use, offering out-of-the-box chart templates and a limited set of customization options.
Integration: Bokeh seamlessly integrates with Python scientific libraries such as NumPy, Pandas, and SciPy, making it a powerful tool for data analysis and visualization in scientific computing. In contrast, Chartify is designed to work specifically with data from pandas DataFrames, simplifying the data preparation process but limiting its compatibility with other libraries.
Community Support: Bokeh has a large and active community of users and contributors, providing extensive documentation, tutorials, and plugins to support users in creating advanced visualizations. Chartify, being a relatively newer tool, may have less extensive community support and resources available for users.
Development Focus: Bokeh is aimed at users who require advanced data visualization capabilities, such as interactive plots, dashboards, and complex visualizations for presentations or web applications. Chartify, on the other hand, targets users who prioritize quick and easy chart creation for data exploration and visualization in a simple and straightforward manner.
Backend Dependency: Bokeh relies on a JavaScript library called Backbone.js for providing structure to web applications, allowing for more sophisticated client-side interactions. Chartify, on the other hand, has minimal dependencies and is designed to be self-contained and straightforward to use without complex backend requirements.

In Summary, Bokeh and Chartify differ in architecture, flexibility, integration, community support, development focus, and backend dependencies.

Bokeh vs Chartify

Overview