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 modulesimport numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as plt |
- Creating dataset
Python3
# creating datasetdf = 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 boxplotsns.boxplot(data=df)# Label x-axisplt.xlabel('Desserts')# labels y-axisplt.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 = Trueindex_sort = df.mean().sort_values().indexindex_sort |
Output:
- Using sorted index we can sort the data frame that we created.
Python3
# now applying the sorted # indices to the datadf_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-axisplt.xlabel('Desserts')# labels y-axisplt.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 modulesimport numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as plt# creating datasetdf = 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 meanindex_sort = df.mean().sort_values().index# now applying the sorted indices to the datadf_sorted = df[index_sort]# plotting the boxplot for the data sns.boxplot(data = df_sorted)# Label x-axisplt.xlabel('Desserts')# labels y-axisplt.ylabel('preference of people') |
Output:
