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doc: add example, bidmensional 3d view #3300

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@caiojp caiojp commented Dec 23, 2023

The chart created in this code is a two-dimensional visualization tool designed to represent three dimensions of data in an intuitive and interactive manner. It uses vehicle data, combining adjusted horsepower (categorized by color), adjusted weight (on the X-axis), and cylinder categories (distinguished in separate charts). This approach allows users to clearly and simply visualize the relationship between these variables, despite the inherent complexity of the data.

Moreover, the chart employs bubble size to represent the count of vehicles in each group, adding an additional layer of data to the analysis. Interactive features, such as selections and filters, enable users to customize the visualization, focusing on specific ranges of horsepower or other characteristics. This enhances the exploration and understanding of the underlying patterns and trends in the vehicle data.

Screenshot 2023-12-23 at 15 55 25 Screenshot 2023-12-23 at 15 55 46 Screenshot 2023-12-23 at 15 56 18

If you have any suggestions for improvement or anything I should change, tell me, I will fix it! Thank you in advance.

The chart created in this code is a two-dimensional visualization tool designed to represent three dimensions of data in an intuitive and interactive manner. It uses vehicle data, combining adjusted horsepower (categorized by color), adjusted weight (on the X-axis), and cylinder categories (distinguished in separate charts). This approach allows users to clearly and simply visualize the relationship between these variables, despite the inherent complexity of the data.

Moreover, the chart employs bubble size to represent the count of vehicles in each group, adding an additional layer of data to the analysis. Interactive features, such as selections and filters, enable users to customize the visualization, focusing on specific ranges of horsepower or other characteristics. This enhances the exploration and understanding of the underlying patterns and trends in the vehicle data.
@mattijn
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mattijn commented Dec 24, 2023

Thanks for adopting this library in your analysis workflows. It is great that you are learning how to use this for complex analysis.
The current pull request is too complex, particularly with the extensive use of pandas for data manipulation and the inclusion of unrelated package imports. Simplifying the example would not only make it more accessible for others but also contribute to a more effective learning experience. Thanks for trying to improve the examples.

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