How to create bubble chart using plotly

by vigneshchennai74 Updated: Nov 9, 2023

Solution Kit

A Bubble Chart, powered by the library, is a dynamic and powerful data visualization tool. Bubble Chart leverages the concept of bubble plots to represent data points.

This visualization technique conveys multidimensional information in a single graph. The visualization technique is essential for effective data visualization in various domains. Visualization technique helps in Data Science and Python Programming environments.

Bubble Charts use a scatter plot structure, with data points as circles on a Cartesian plane. We can define each bubble by its position on the x and y axes, color, and size. This technique enables the representation of many variables. We can visualize by adding depth and meaning to the data. The library's features enable the creation of a simple bubble chart. It can communicate insights from diverse datasets.

Bubble Charts are versatile. They can visualize numeric data, categorical data, and time series data. Numeric variables can determine the placement of data points on the chart. In contrast, categorical variables can influence bubble color. We can represent time series data over time.

Bubble Charts provide the flexibility to choose from a variety of bubble sizes. The size of the bubbles can represent a third variable, adding an extra layer of information.

Bubble charts offer a plethora of chart types to enrich data visualization. You can include bar charts, line charts, and scatter plots. We can combine these chart types with bubble charts. This creates interactive dashboards, allowing for the simultaneous display of many charts.

Bubble Charts offers interactive features that enhance user engagement. These include drag-and-drop functionality for rearranging data points. They also offer copy-and-paste capabilities for quick data manipulation. This enables a dynamic data exploration experience, including reordering plots.

The library facilitates the export of Bubble Charts in PDF, Excel, and JSON files. This allows users to share their visualizations or perform further offline data analysis.

There are many advantages to using Bubble Charts for data visualization. This library simplifies conveying complex data relationships, making insights accessible to all. Versatility, many chart types, and interactive features enable comprehensive data exploration. Bubble Charts offers a sophisticated data presentation method. In summary, the library's Bubble Charts are a must-have tool. It helps those looking to harness the full potential of data visualization.

Preview of the output that you will get on running this code from your IDE

Code

In this solution we have used Tornado library

How to place bubble charts in an image using Plotly?

PythonLines of Code : 59License : Strong Copyleft (CC BY-SA 4.0)

import numpy as np
import pandas as pd

from PIL import Image

import plotly.express as px
import plotly.graph_objects as go

## create some sample data
np.random.seed(42)
dt_lap = pd.DataFrame({
    "time": list(pd.date_range(start='16/11/2012 11:20', periods=30, freq="1min"))*3,
    "power": np.random.randint(low=0, high=50, size=90),
    "device": ["Laptop"]*30+["Fridge"]*30+["Kettle"]*30
})
dt_lap["time_string"] = dt_lap["time"].astype(str)

## coordinate mapping:
device_coordinate_map = {
    'Laptop': [0.18, 0.8],
    'Fridge': [0.67, 0.5],
    'Kettle': [0.88, 0.40]
}

dt_lap['x'], dt_lap['y'] = zip(*list(dt_lap['device'].map(device_coordinate_map).values))

## load image
img = Image.open('room_img.png')

# Create figure
fig = px.scatter(dt_lap, x="x", y="y",
             size="power", color="device",animation_frame="time_string",animation_group="device",
                   height=600,
    width=800, hover_name="time")         

# Add images
fig.add_layout_image(
    dict(
        source=img,
        xref="paper",
        yref="paper",
        x=0,
        y=1,
        sizex=1,
        sizey=1,
        sizing="contain",
        layer="below"
    )
)

# Set templates
fig.update_layout(
    template="plotly_white", 
    xaxis=dict(range=[0,1], showgrid=False),
    yaxis=dict(range=[0,1], showgrid=False)
)

fig.show()

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Open VS Code and create a new file in the editor.
Copy the code snippet that you want to run, using the "Copy" button or by selecting the text and using the copy command (Ctrl+C on Windows/Linux or Cmd+C on Mac).,
Paste the code into your file in VS Code, and save the file with a meaningful name and the appropriate file extension for Python use (.py).file extension.
To run the code, open the file in VS Code and click the "Run" button in the top menu, or use the keyboard shortcut Ctrl+Alt+N (on Windows and Linux) or Cmd+Alt+N (on Mac). The output of your code will appear in the VS Code output console.
Paste the code into your file in VS Code.
Save the file with a meaningful name and the appropriate file extension for Python use (.py)
In line 28 img = Image.open() GIve the correct image path to run the code.
Save and run the Code

I hope this is useful to you. I have added the version information in the following section. I found this code snippet by searching " Howto do unittest for tornado + async def? " in Kandi. you can try any use case.

Environment Tested

I tested this solution in the following versions. Please be aware of any changes when working with other versions.

The solution is created and tested using Vscode 1.77.2 version
The solution is created in Plotly 5.18.0 version
The solution is created in Python 3.7.15 version

This code explains How to create a bubble chart using plotly. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help. How to create a bubble chart using Plotly in Python.

Dependent Library

dashby plotly

Python

18829

Version:v2.10.2

License: Permissive (MIT)

Data Apps & Dashboards for Python. No JavaScript Required.

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dashby plotly

Python 18829 Version:v2.10.2 License: Permissive (MIT)

Data Apps & Dashboards for Python. No JavaScript Required.

Support

Quality

Security

License

Reuse

If you do not have the tornado that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Flask page in Kandi.

You can search for any dependent library on Kandi like Plotly

FAQ

1. How do Plotly Bubble Charts enhance data visualization?

Bubble Charts enhance data visualization by displaying complex data relationships with simplicity. They allow for representing many variables in a single chart, making insights accessible.

2. What are the key components of Plotly Graph Objects for creating a bubble chart?

Graph Objects for a bubble chart include the x and y axes for data positioning.
Bubble size for an extra layer of information
Bubble color for categorical representation.

3. How do you choose the right bubble color in a Plotly Bubble Chart?

Choose bubble colors that best represent your data. We can do it by considering the categorical or qualitative nature of the data. Use color scales and schemes that align with the information you want to convey.

4. What other types of charts can you create with the library besides Bubble Charts?

The library offers various chart types. It includes bar charts, line charts, scatter plots, and more. We can combine it for interactive and dynamic data visualization.

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