How to Make Boxplots with Data Points using Seaborn in Python?

Prerequisites :

• Seaborn
• Matplotlib

Box Plot or a Whisker Plot is a statistical plot to visualize graphically, depicting group of numerical data through their quartiles. This plot displays the summary of set of data containing the five values known as minimum, quartile 1, quartile 2 or median, quartile 3 and maximum, where the box is drawn from first quartile to third quartile.

A generic box plot mainly focuses on the five elements mentioned above to give the user a quartile based interpretation of the data, but it is possible to show data points on the box plot itself, making it more informative. For this seaborn is equipped with stripplot() function, all we have to do is call it just after boxplot() function with appropriate parameters to generate a boxplot with data points.

A strip plot is drawn on its own. It is a good complement to a boxplot or violinplot in cases where all observations are shown along with some representation of the underlying distribution. It is used to draw a scatter plot based on the category.

Syntax: seaborn.stripplot(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, jitter=True, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor=’gray’, linewidth=0, ax=None, **kwargs)

Parameters:

• x, y, hue: Inputs for plotting long-form data.
• data: Dataset for plotting.
• order: It is the order to plot the categorical levels in.
• color: It is the color for all of the elements, or seed for a gradient palette

Returns: This method returns the Axes object with the plot drawn onto it.

Approach:

• Import the library
• Create or load the dataset.
• Plot a boxplot using boxplot().
• Add data points using stripplot().
• Display plot.

Given below are few implementations to help you understand better 

Example 1: Regular box plot for comparison

Python

# importing library
import seaborn as sns
import matplotlib.pyplot as plt

# loading seaborn dataset tips
tdata = sns.load_dataset('tips')

# creating boxplot
sns.boxplot(x='size', y='tip', data=tdata)

# display plot
plt.show()

Output:

Example 2: Creating box plot with data points

Python

# importing library
import seaborn as sns
import matplotlib.pyplot as plt
# loading seaborn dataset tips
tdata = sns.load_dataset('tips')

# creating boxplot
sns.boxplot(x='size', y='tip', data=tdata)

# adding data points
sns.stripplot(x='size', y='tip', data=tdata)
# display plot
plt.show()

Output:

Example 3: Boxplot with data points with non-default color

Python

# importing library
import seaborn as sns
import matplotlib.pyplot as plt
# loading seaborn dataset tips
tdata = sns.load_dataset('tips')

# creating boxplot
sns.boxplot(x='size', y='tip', data=tdata)

# adding data points
sns.stripplot(x='size', y='tip', data=tdata, color="grey")
# display plot
plt.show()

Output:

Example 4: Changing size of the data points

Python

# importing library
import seaborn as sns
import matplotlib.pyplot as plt
# loading seaborn dataset tips
tdata = sns.load_dataset('tips')

tdata = tdata.head(10)
# creating boxplot
sns.boxplot(x='size', y='tip', data=tdata)

# adding data points
sns.stripplot(x='size', y='tip', data=tdata, color="grey", size=8)
# display plot
plt.show()

Output:

Example 5: Plotting transparent data points

Python

# importing library
import seaborn as sns
import matplotlib.pyplot as plt
# loading seaborn dataset tips
tdata = sns.load_dataset('tips')

tdata = tdata.head(20)
# creating boxplot
sns.boxplot(x='size', y='tip', data=tdata)

# adding data points
sns.stripplot(x='size', y='tip', data=tdata, color="black", size=8, alpha=0.5)
# display plot
plt.show()

Output: