Box plot visualization with Pandas and Seaborn

Box Plot is the visual representation of the depicting groups of numerical data through their quartiles. Boxplot is also used for detect 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 a sample data using 25th, 50th and 75th percentiles. These percentiles are also known as the lower quartile, median and upper quartile. A box plot consist of 5 things.

• Minimum
• First Quartile or 25%
• Median (Second Quartile) or 50%
• Third Quartile or 75%
• Maximum

To download the dataset used, click here. Draw the box plot with Pandas: One way to plot boxplot using pandas dataframe is to use boxplot() function that is part of pandas library. Python3

# import the required library 
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
% matplotlib inline


# load the dataset
df = pd.read_csv("tips.csv")

# display 5 rows of dataset
df.head()   

Boxplot of days with respect total_bill. Python3

df.boxplot(by ='day', column =['total_bill'], grid = False)

  Boxplot of size with respect tip. Python3

df.boxplot(by ='size', column =['tip'], grid = False)

  Draw the boxplot using seaborn library:

Syntax : seaborn.boxplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, fliersize=5, linewidth=None, whis=1.5, notch=False, ax=None, **kwargs) Parameters: x = feature of dataset y = feature of dataset hue = feature of dataset data = dataframe or full dataset color = color name

Let's see how to create the box plot through seaborn library. Information about "tips" dataset. Python3

# load the dataset
tips = sns.load_dataset('tips')

tips.head()

Boxplot of days with respect total_bill. Python3

# Draw a vertical boxplot grouped 
# by a categorical variable:
sns.set_style("whitegrid")

sns.boxplot(x = 'day', y = 'total_bill', data = tips)