Chartify vs Matplotlib

Overview

Matplotlib

Stacks1.6K

Followers336

Votes11

Chartify

Stacks17

Followers25

Votes0

GitHub Stars3.6K

Forks335

Chartify vs Matplotlib: What are the differences?

<Write Introduction here>

Ease of Use: Chartify is designed to be simple and easy to use, offering a more user-friendly experience compared to Matplotlib, which requires a steeper learning curve due to its extensive customization options and complex syntax.
Interactive Features: Chartify provides built-in interactivity features that allow users to create interactive visualizations with minimal effort, while Matplotlib requires more advanced code to achieve similar interactive capabilities.
Data Preparation: Chartify simplifies data preparation by automatically handling datetime formatting, data aggregation, and other common tasks, making it suitable for users who prefer a more streamlined approach. In contrast, Matplotlib requires users to manually process and structure the data before plotting, which can be more time-consuming.
Styling and Themes: Matplotlib offers a wide range of styling options and themes for customizing visualizations in detail, providing more flexibility for advanced users who require precise control over the appearance of their plots. Chartify, on the other hand, focuses on simplicity and offers a limited selection of styling options.
Integration with Pandas DataFrames: Chartify seamlessly integrates with Pandas DataFrames, allowing users to create visualizations directly from their data without the need for extensive data manipulation. Matplotlib can also work with Pandas DataFrames, but users may need to handle data formatting and transformation more manually.
Support and Documentation: Matplotlib has a larger user base and more extensive documentation available online, making it easier to find resources and solutions to common problems. In comparison, Chartify, being a newer tool, may have fewer resources and community support, which could potentially impact troubleshooting and learning opportunities for users.

In Summary, Chartify offers a user-friendly and streamlined approach to creating interactive visualizations, while Matplotlib provides a more customizable and feature-rich environment for advanced users.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Detailed Comparison

Matplotlib	Chartify
It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.	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.
Statistics
GitHub Stars -	GitHub Stars 3.6K
GitHub Forks -	GitHub Forks 335
Stacks 1.6K	Stacks 17
Followers 336	Followers 25
Votes 11	Votes 0
Pros & Cons
Pros 11 The standard Swiss Army Knife of plotting Cons 5 Lots of code	No community feedback yet
Integrations
No integrations available	Python

What are some alternatives to Matplotlib, 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

Chartify vs Matplotlib: What are the differences?

<Write Introduction here>

Ease of Use: Chartify is designed to be simple and easy to use, offering a more user-friendly experience compared to Matplotlib, which requires a steeper learning curve due to its extensive customization options and complex syntax.
Interactive Features: Chartify provides built-in interactivity features that allow users to create interactive visualizations with minimal effort, while Matplotlib requires more advanced code to achieve similar interactive capabilities.
Data Preparation: Chartify simplifies data preparation by automatically handling datetime formatting, data aggregation, and other common tasks, making it suitable for users who prefer a more streamlined approach. In contrast, Matplotlib requires users to manually process and structure the data before plotting, which can be more time-consuming.
Styling and Themes: Matplotlib offers a wide range of styling options and themes for customizing visualizations in detail, providing more flexibility for advanced users who require precise control over the appearance of their plots. Chartify, on the other hand, focuses on simplicity and offers a limited selection of styling options.
Integration with Pandas DataFrames: Chartify seamlessly integrates with Pandas DataFrames, allowing users to create visualizations directly from their data without the need for extensive data manipulation. Matplotlib can also work with Pandas DataFrames, but users may need to handle data formatting and transformation more manually.
Support and Documentation: Matplotlib has a larger user base and more extensive documentation available online, making it easier to find resources and solutions to common problems. In comparison, Chartify, being a newer tool, may have fewer resources and community support, which could potentially impact troubleshooting and learning opportunities for users.

Chartify vs Matplotlib

Overview