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How to create a bubble chart using matplotlib Python?

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by sneha@openweaver.com dot icon Updated: May 10, 2023

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A bubble chart is a chart type that uses circles to represent data points. We can scale each circle, or "bubble," based on the data point's value relative to other data points. Bubble charts can analyze data in various ways. Bubble charts visualize the relationship between three or more variables. We can plot three variables on three axes—x, y, and bubble size—to show the correlation between the data points. The bubble size represents the third variable, which may be a measure of importance. We can also represent the population size or scale measures, like revenue or profit.


To create a bubble chart, you must import Python modules like numpy and matplotlib. It can use the scatter function to plot the data. Plotly allows you to create interactive graphs to compare different data sets. You can customize the bubble size and color. You can also create a single bubble representing each data point. 


Pie charts represent the proportion of each data point relative to the whole. We can size each bubble according to the percentage of the total. This chart type is useful for comparing data categories and highlighting the values. Bar charts are another form of a bubble chart. It compares data points across categories. This chart type can compare different categories of data and show trends over time.


We can scale each bubble according to its value relative to the other data points. Scatter plots are a type of bubble chart. It helps visualize the relationship between two variables. We can plot each bubble according to its value on both axes. The bubble size indicates the strength of the relationship between the two variables. Scatter plots are useful for identifying correlations and trends in data. 


They visualize a project or process's timeline and the tasks we must complete. We can use Gantt charts in project management. Bubble maps are a type of bubble chart used to show the geographic location of data points. We can place each bubble on a map according to its coordinates; its size indicates the data point's value. This chart type is useful for visualizing data distribution across a geographic area. 


We can visualize the data on a bubble chart in several different ways. 

  • Bubble Color: Different colors can represent different data points or categories. 
  • Bubble Size: The bubble size can indicate the magnitude of the data points. 
  • Bubble Shape: Different shapes can represent different data points or categories. 
  • Bubble Position: The position can indicate the relationship between the data points. 
  • Bubble Contours: Contours can show the density of the data points in each area. 


When creating a bubble chart, using a consistent color scheme is important. It will help viewers distinguish between different data points. Additionally, the bubble size should be proportional to the data point's value. This will help viewers understand the relative magnitude of each data point. We can include labels to identify the data points and to provide extra context. Finally, keeping the chart simple and the data manageable is important. It is because this makes it difficult to interpret. 


Bubble charts can communicate data in a variety of ways. They can display trends, such as population growth or stock market performance. They can compare data points, such as countries' GDPs or companies' revenues. They can visualize the relationship between two numeric variables. It will show the distribution of data points along the x- and y-axis. They can also represent the relationship between a third or fourth variable. It can be the size or color, using bubbles of different sizes or colors. Bubble charts can compare data sets and compare different groups. It can also demonstrate trends over time. We can use it for data analysis and data interpretation. 


Bubble charts can explore data patterns like changes in population between variables. They can find insights that may not be apparent at first glance. A bubble chart can help reveal relationships between different categories of data. It can take the number of universities in different countries. It can also take the number of products sold in different markets. Finally, bubble charts can identify correlations between different variables. It can be the relationship between a company's stock price and revenue. 


Bubble charts help visualize data because they are easy to use. They are a great choice for presentations and other visualizations. It provides a more visually appealing way to communicate data. Bubble charts can explore patterns in data. It can identify outliers or compare different data points. It can help viewers understand the relative magnitude of each data point. Finally, bubble charts can identify correlations between different variables. We can do it by allowing viewers to gain insights that may not be apparent at first glance. 




Fig1: Preview of the Code



Fig2: Preview of Output when the code is run in IDE.

Code


In this solution, we're creating a bubble chart using matplotlib python

Making a non-overlapping bubble chart in Matplotlib (circle packing)

PythondotLines of Code : 71dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
if (e < self.E and self.isvalid(i)):

self.p = lambda x,y: np.sum((x**2+y**2)**2)

import numpy as np
import matplotlib.pyplot as plt

# create 10 circles with different radii
r = np.random.randint(5,15, size=10)

class C():
    def __init__(self,r):
        self.N = len(r)
        self.x = np.ones((self.N,3))
        self.x[:,2] = r
        maxstep = 2*self.x[:,2].max()
        length = np.ceil(np.sqrt(self.N))
        grid = np.arange(0,length*maxstep,maxstep)
        gx,gy = np.meshgrid(grid,grid)
        self.x[:,0] = gx.flatten()[:self.N]
        self.x[:,1] = gy.flatten()[:self.N]
        self.x[:,:2] = self.x[:,:2] - np.mean(self.x[:,:2], axis=0)

        self.step = self.x[:,2].min()
        self.p = lambda x,y: np.sum((x**2+y**2)**2)
        self.E = self.energy()
        self.iter = 1.

    def minimize(self):
        while self.iter < 1000*self.N:
            for i in range(self.N):
                rand = np.random.randn(2)*self.step/self.iter
                self.x[i,:2] += rand
                e = self.energy()
                if (e < self.E and self.isvalid(i)):
                    self.E = e
                    self.iter = 1.
                else:
                    self.x[i,:2] -= rand
                    self.iter += 1.

    def energy(self):
        return self.p(self.x[:,0], self.x[:,1])

    def distance(self,x1,x2):
        return np.sqrt((x1[0]-x2[0])**2+(x1[1]-x2[1])**2)-x1[2]-x2[2]

    def isvalid(self, i):
        for j in range(self.N):
            if i!=j: 
                if self.distance(self.x[i,:], self.x[j,:]) < 0:
                    return False
        return True

    def plot(self, ax):
        for i in range(self.N):
            circ = plt.Circle(self.x[i,:2],self.x[i,2] )
            ax.add_patch(circ)

c = C(r)

fig, ax = plt.subplots(subplot_kw=dict(aspect="equal"))
ax.axis("off")

c.minimize()

c.plot(ax)
ax.relim()
ax.autoscale_view()
plt.show()

Instructions

Follow the steps carefully to get the output easily.

  1. Install Idle Python on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install Numpy - pip install numpy
  4. Install matplotlib - pip install matplotlib
  5. Copy the snippet using the 'copy' button and paste it into that file.
  6. Remove/Comment out the first two ines of the code to avoid getting an error.
  7. Run the file using run button.


I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.


I found this code snippet by searching for "bubble chart using matplotlib python" in kandi. You can try any such use case!

Dependent Libraries

matplotlibby matplotlib

Python doticonstar image 17559 doticonVersion:v3.7.1doticon
no licences License: No License (null)

matplotlib: plotting with Python

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            matplotlibby matplotlib

            Python doticon star image 17559 doticonVersion:v3.7.1doticonno licences License: No License

            matplotlib: plotting with Python
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                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
                                Support
                                  Quality
                                    Security
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                                          FAQ  

                                          What is a Python Bubble Chart, and how does it work?  

                                          A Python Bubble Chart is a data visualization tool. It uses bubbles of varying sizes to represent different data points. We can determine the size of the bubble by the associated data value. We should use it with larger bubbles representing larger data values. This chart type is useful for visualizing data with many variables. We can do it by allowing viewers to identify patterns and trends. It can compare data points, as we can sort the bubbles to compare data values. 


                                          How can I use Plotly to create bubble charts in Python?  

                                          Plotly is a powerful data visualization library. We can create bubble charts in Python. To use Plotly to create a bubble chart, you must first import the plotly library. Then we can define the data points you wish to plot. Next, you must define the size of each bubble, as well as the color, text, and other properties of each bubble. Finally, you must call the plotly.graph_objs.scatter function. This function allows you to define the x and y axes and extra parameters, like hovertext and marker. It helps create the bubble chart. 


                                          What is the difference between scatter plots and bubble charts?  

                                          Scatter plots are data visualizations. We can use it as small dots to represent the data points. We can plot each dot according to its x and y values, and the dot size does not represent any extra information. Bubble charts are data visualizations. It uses bubbles of varying sizes to represent different data points. We can determine the bubble size by the associated data value. We can do it with larger bubbles representing larger data values. 


                                          How do I adjust the size of my bubbles for different data points plotly? 

                                          When creating a bubble chart in plotly, you can adjust the size of the bubbles for different data points. We can do it by using the sizer of the parameter in plotly.graph_objs.scatter function. This parameter allows you to specify the bubbles' minimum and maximum sizes. We can adjust each bubble's size accordingly. 


                                          Are interactive graphs possible with Python Bubble Charts?  

                                          Yes, interactive graphs are possible with Python Bubble Charts. Plotly is a powerful data visualization library. It can create interactive bubble charts. It will allow you to hover over data points to see extra information. It will even click on data points to open new windows with extra information. With Plotly, you can create interactive charts. 


                                          Is there a good tutorial or guide that explains how to make bubble charts using Python?  

                                          Many excellent tutorials and guides explain how to make bubble charts using Python. This tutorial provides a step-by-step guide to creating a bubble chart. Additionally, the Python Bubble Chart page on the official website provides detailed instructions. It will help you understand how to create bubble charts in Python. 


                                          Can I tune marker appearance when making a bubble chart in Python?  

                                          Yes, when creating a bubble chart in Python. You can tune the marker's appearance. You can do it using the marker parameter in the plotly.graph_objs.scatter function. This parameter helps specify the shape, color, size, and other properties. 


                                          What are some alternatives to using a Bubble Chart, such as Line Plots or other types of plots?  

                                          Besides bubble charts, many other data visualization tools can visualize data. Some alternatives to bubble charts include lines, bars, scatter, and histograms. All these data visualization tools can visualize data differently. Choosing the one that best suits your data and the message you want to communicate is important. 


                                          Does Plotly offer an easy way to add color scales to my bubble chart in Python?  

                                          Yes, Plotly does offer an easy way to add color scales to bubble charts in Python. You can use the marker.colorscale parameter in the plotly.graph_objs.scatter function. This parameter allows you to specify the color scale you wish to use. We can adjust the colors of the bubbles accordingly. 


                                          How can I represent quantitative variables on my bubble chart using Plotly in Python? 

                                          When creating a chart, you can represent variables by adjusting the bubble size. To do so, you must use the sizeref parameter in the plotly.graph_objs.scatter function. This parameter allows you to specify the bubbles' minimum and maximum sizes. We can adjust each bubble's size accordingly. 

                                          You can also search for any dependent libraries on kandi like "matplotlib / numpy"

                                          Environment Tested


                                          I tested this solution in the following versions. Be mindful of changes when working with other versions.

                                          1. The solution is created in Python3.9.6.
                                          2. The solution is tested on numpy 1.21.5 version.


                                          Using this solution, we are able to create a bubble chart using matplotlib python.


                                          This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to create a bubble chart using matplotlib python.

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