Sort Boxplot by Mean with Seaborn in Python

Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to make statistical plots more attractive. It is built on the top of matplotlib library and also closely integrated to the data structures from pandas.
Box Plot is the visual representation of the depicting groups of numerical data through their quartiles. Boxplot is also used for detecting the outlier in data set. It captures the summary of the data efficiently with a simple box and whiskers and allows us to compare easily across groups. Boxplot summarizes sample data using 25th, 50th, and 75th percentiles. These percentiles are also known as the lower quartile, median and upper quartile.

Sometimes, we want to order the boxplots according to our needs there are many ways you can order a boxplot that are:

• Order of boxplot manually
• Sorting of boxplot using mean

In this article, we will discuss how to order a boxplot using mean.

What sort boxplot using mean?

When we have multiple groups it’s suggested to use sorting by mean or median manually it will get difficult to sort.

Step-by-step Approach:

• Importing Libraries

Python3

# import required modules
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

• Creating dataset

Python3

# creating dataset
df = pd.DataFrame({
    'Ice-cream': np.random.normal(57, 5, 100),
    'Chocolate': np.random.normal(73, 5, 100),
    'cupcake': np.random.normal(68, 8, 100),
    'jamroll': np.random.normal(37, 10, 100),
    'cake': np.random.normal(76, 5, 100),

})
df.head()

Output:

• Plot the data before sorting the boxplot.

Python3

# plot the data into boxplot

sns.boxplot(data=df)

# Label x-axis
plt.xlabel('Desserts')

# labels y-axis
plt.ylabel('preference of people')

Output:

• Now sort the data first and get the sorted indices as we have to  sort the boxplot using mean, so we will apply the mean() and sort_values function to the data.

Python3

# This will give the indices of the sorted
# values into the ascending order the default
# value in sort_values is ascending = True
index_sort = df.mean().sort_values().index
index_sort

Output:

• Using sorted index we can sort the data frame that we created.

Python3

# now applying the sorted
# indices to the data
df_sorted = df[index_sort]

So We have sorted the data let’s plot the boxplot of the data.

Python3

# plotting the boxplot for the data
sns.boxplot(data = df_sorted)

# Label x-axis
plt.xlabel('Desserts')

# labels y-axis
plt.ylabel('preference of people')

Output:

If one wants to sort in descending order then use the below syntax: 

index_sort = df.mean().sort_values(ascending=False).index

Below is the complete program based on the above approach:

Python3

# import required modules
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt


# creating dataset
df = pd.DataFrame({
    'Ice-cream': np.random.normal(57, 5, 100),
    'Chocolate': np.random.normal(73, 5, 100),
    'cupcake': np.random.normal(68, 8, 100),
    'jamroll': np.random.normal(37, 10, 100),
    'cake': np.random.normal(76, 5, 100),

})


# sort on the basis of mean
index_sort = df.mean().sort_values().index

# now applying the sorted indices to the data
df_sorted = df[index_sort]


# plotting the boxplot for the data
sns.boxplot(data = df_sorted)

# Label x-axis
plt.xlabel('Desserts')

# labels y-axis
plt.ylabel('preference of people')

Output: