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This article is to share with you what the essential visual chemical industry of Python has. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

1. Visualization of lantern fruit

Dengguo data visualization BI software is a new generation of artificial intelligence data visualization large screen software, built-in rich large screen template, visual editing operation, you can create your own large screen without any experience, and is widely used in commercial, economic, medical and other fields.

Dengguo Visualization focuses on large-screen data visualization applications, and deeply optimizes the production and sharing process of visual large screens. Although on the surface, it is a drag-and-drop editing method, compared with other BI products, it is easier to use Dengguo to make a large screen, and the user experience is better.

The software provides 50 + visualization chart components, supports custom components and custom animation, can meet a variety of visualization needs, and produces a variety of cool visualization screens. Dengguo large screen template Mall provides 100 + visual large screen templates, covering various industries, replacing data with one click to generate a large screen.

The software supports the client software of the Windows/macOS/Linux system, and all operations are carried out at the local terminal, which is more secure than SAAS and does not require separate privatized deployment. At the same time, Dengguo also supports local area network version and OEM version to meet your various needs.

2. Jupyter: big data's visual one-stop shop

JupyteR is an open source project that implements real-time collaboration in big data's analysis, visualization and software development through more than a dozen programming languages. Its interface contains a code input window and runs the entered code to provide visually readable images based on the selected visualization technology.

However, the functions mentioned above are only the tip of the iceberg. Jupyter Notebook can be shared by teams to achieve internal collaboration and to facilitate team collaboration for data analysis. The team can upload Jupyter Notebook to GitHub or Gitlab so that they can work together to influence the results. Teams can use Kubernetes to include Jupyter Notebook in the Docker container, or they can run Notebook on any other machine that uses Jupyter. When Python and R were first used, Jupyter Notebook was actively introducing kernels encoded by other programming languages, such as Java,Go,C#,Ruby.

In addition, Jupyter is able to interact with multiple frameworks such as Spark, which enables Jupyte to provide a versatile solution for data processing of large amounts of dense data collected from programs with different input sources.

3. Tableau:AI, big data and the best solution for visualization of machine learning applications.

Tableau is one of the market leaders in big data Visualization and is particularly efficient in providing interactive data visualization for big data operations, deep learning algorithms and many types of AI applications. Tableau can work with Amazon AWS,MySQL,Hadoop,Teradata and SAP to make it a versatile tool that can create detailed graphics and display intuitive data. This allows senior managers and intermediate chain managers to make basic decisions based on Tableau graphics that contain a lot of information and are easy to read.

4. Free and powerful integration features supported by Google Chart:Google

Google is synonymous with leadership today. Just as Google browser is currently the most popular browser, Google Chart is one of the best solutions for big data visualization, not to mention it is completely free and has received strong technical support from Google. Why is it supported by Google? Because the data analyzed by Google Chart is obviously used to train AI developed by Google, such cooperation is win-win for all parties. Google Chart provides a large number of visualization types, from simple pie charts to time series to multi-dimensional interaction matrices. The chart has many options to adjust. If you need to customize the chart deeply, you can refer to the detailed help section. The tool renders the generated charts in HTML5 / SVG, so they are compatible with any browser. Google Chart's support for VML ensures its compatibility with older versions of IE, and charts can be migrated to the latest versions of Android and iOS. More importantly, Google Chart combines data from a variety of Google services such as Google Maps. The generated interactive chart can not only input data in real time, but also can be controlled by interactive dashboard.

5. D3.js: visually display big data in any way you need

D3.js stands for Data Driven Document, a JS library for real-time interactive big data visualization. Since this is not a tool, users need to have a good understanding of Javascript and be able to present it in a form that can be understood by others before using it to process data. In addition, the JS library renders data in SVG and HTML5 formats, so older browsers like IE7 and 8 cannot take advantage of D3.js functionality. Data collected from different sources, such as large-scale data, will be bound to real-time DOM and generate interactive animations (2D and 3D) at breakneck speed. The D3 architecture allows users to reuse code intensively through a variety of attachments and plug-ins.

6 、 Tangle

Tangle is a Java library and tool that not only visualizes, but also allows designers and developers to create reactive programs that provide a deep understanding of the relationships between data. For example, a web-side conversion calculator can convert currencies or measurements.

Thank you for reading! This is the end of this article on "what are the essential visual chemicals of Python?". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, you can share it for more people to see!

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