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Horizontal Boxplots with Points using Seaborn in Python
  • Last Updated : 26 Nov, 2020

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 into the data structures from pandas.
Seaborn aims to make visualization of the central part of exploring and understanding data. It provides dataset-oriented APIs, so that we can switch between different visual representations for the same variables for a better understanding of the dataset.

Why horizontal Boxplots will be used instead of boxplots?

They are useful when you have numerous groups with larger names. Boxplots works fine when we have a limited number of groups and short names of groups. But if we have longer titles they would overlap each other in boxplots, and it will get difficult to read the data and taking insights from the graph. Horizontal boxplots solve that problem very easily. 
 

Below is the step-by-step Approach to illustrate Horizontal Boxplots using Seaborn module:

  • Import libraries

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

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  • Assign data 

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# To avoid changing values everytime you run the cell
np.random.seed(42)
  
# Creating Data
df = pd.DataFrame({
    'Ice-cream': np.random.normal(40, 15, 100),
    'Chocolate': np.random.normal(60, 10, 100),
    'Cakes': np.random.normal(80, 5, 100)
})
  
# Display data
print(df)

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Output:



Mostly data we come across are in wide format. For example when you scrape a website. This data is in the wide form Convert the data into long-form by using pandas melt function. 

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# Using melt dataframe for Converting data to long form
data_df = df.melt(var_name='Dessert', value_name='Votes')
  
# Data is in long form
print(data_df.head())

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Output:

  • Plot this data into a boxplot.

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# Adjust size
plt.figure(figsize=(8.3,6))
  
# Illustrate boxplot
sns.boxplot(y="Dessert", x="Votes", data=data_df)

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Output:



After plotting the data points into the boxplot. Strip plot can also be drawn on its own but it will complement while using with boxplot or violin plot. 

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# It will create the data points inside the boxplot
# Illustrating box plot
sns.boxplot(y="Dessert", x="Votes", data=data_df)
  
# Illustrating strip plot
sns.stripplot(y="Dessert", x="Votes", color='black',
              alpha=0.3, data=data_df)

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Output:

Changing our boxplot into the horizontal boxplot with data points by simply swapping the axes when we have numerous columns then names overlap with each other, so then we use a horizontal boxplot.

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# You can simply change into horizontal 
# boxplots by swapping  x and y axes.
# Illustrating box plot
sns.boxplot(x="Dessert", y="Votes", data=data_df)
  
# Illustrating strip plot
sns.stripplot(x="Dessert", y="Votes", color='black',
              alpha=0.3, data=data_df)

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Output:

Below is the complete program based on the above approach:

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# Import required modules
import sklearn
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
  
  
# To avoid changing values everytime you run the cell
np.random.seed(42)
  
  
# Creating Data
df = pd.DataFrame({
    'Ice-cream': np.random.normal(40, 15, 100),
    'Chocolate': np.random.normal(60, 10, 100),
    'Cakes': np.random.normal(80, 5, 100)
})
  
  
# Using melt dataframe for Converting data to long form
data_df = df.melt(var_name='Dessert', value_name='Votes')
  
  
# Adjust size
plt.figure(figsize=(8.3, 6))
  
  
# Assign title
plt.title('Horizontal Boxplots with Points using Seaborn')
  
  
# Illustrating box plot
sns.boxplot(y="Dessert", x="Votes", data=data_df)

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Output:
 


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