Custom your bubble plot

The previous post describes how to plot a basic bubble plot. This post aims to show how you can customize the features of your basic bubble plot with several examples.

The next post describes how to map a color to your bubble plot.

Color

After plotting a basic bubble plot with the scatter() function of matplotlib, you can customize it by changing the color of the markers. You can use the color parameter c for this purpose.

# libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# create data
df = pd.DataFrame({
   'x': np.random.rand(10),
   'y': np.random.rand(10),
   'z': np.random.rand(10),
})
df = pd.DataFrame({
   'x': np.random.rand(5),
   'y': np.random.rand(5),
   'z': np.random.rand(5),
})
 
# Change color with c and alpha
plt.scatter(df['x'], df['y'], s=df['z']*4000, c="red", alpha=0.4)

# show the graph
plt.show()

Shape

As you can change the color of the markers, it is also possible to change the shapes by giving marker parameter to the scatter() function.

# libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# create data
df = pd.DataFrame({
   'x': np.random.rand(10),
   'y': np.random.rand(10),
   'z': np.random.rand(10),
})
df = pd.DataFrame({
   'x': np.random.rand(5),
   'y': np.random.rand(5),
   'z': np.random.rand(5),
})
 
# plot
plt.scatter(df['x'], df['y'], s=df['z']*4000, marker="D")

# show the graph
plt.show()

Global Size

In order to change the size of each marker, s size parameter can be used. In the example below, s parameter is set as a multiplier of z data points, so the sizes of the markers depends on the z values.

# libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# create data
df = pd.DataFrame({
   'x': np.random.rand(10),
   'y': np.random.rand(10),
   'z': np.random.rand(10),
})
df = pd.DataFrame({
   'x': np.random.rand(5),
   'y': np.random.rand(5),
   'z': np.random.rand(5),
})
 
# plot
plt.scatter(df['x'], df['y'], s=df['z']*200)

# show the graph
plt.show()

Edges

linewidth parameter is useful to set the edge thickness of the markers in a basic bubble plot built with matplotlib.

# libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# create data
df = pd.DataFrame({
   'x': np.random.rand(10),
   'y': np.random.rand(10),
   'z': np.random.rand(10),
})
 
# plot
plt.scatter(df['x'], df['y'], s=df['z']*4000, c="green", alpha=0.4, linewidth=6)

# show the graph
plt.show()

Seaborn Style

It is possible to benefit from seaborn library style when plotting charts in matplotlib. You just need to load the seaborn library and use seaborn set_theme() function!

# libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# create data
df = pd.DataFrame({
   'x': np.random.rand(10),
   'y': np.random.rand(10),
   'z': np.random.rand(10),
})
 
# pimp your plot with the seaborn style
import seaborn as sns
sns.set_theme()

# plot
plt.scatter(df['x'], df['y'], s=df['z']*4000, c="green", alpha=0.4, linewidth=6)
 
# Add titles (main and on axis)
plt.xlabel("the X axis")
plt.ylabel("the Y axis")
plt.title("A bubble plot", loc="left")

# show the graph
plt.show()

Going further

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