Skip to content
Related Articles

Related Articles

Python – seaborn.regplot() method

View Discussion
Improve Article
Save Article
Like Article
  • Last Updated : 01 Aug, 2020

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps resolve the two major problems faced by Matplotlib; the problems are ?

  • Default Matplotlib parameters
  • Working with data frames

As Seaborn compliments and extends Matplotlib, the learning curve is quite gradual. If you know Matplotlib, you are already half-way through Seaborn.

seaborn.regplot() :

This method is used to plot data and a linear regression model fit. There are a number of mutually exclusive options for estimating the regression model. For more information click here.

Syntax : seaborn.regplot( x,  y,  data=None, x_estimator=None, x_bins=None,  x_ci=’ci’, scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, order=1, logistic=False, lowess=False, robust=False, logx=False, x_partial=None, y_partial=None, truncate=False, dropna=True, x_jitter=None, y_jitter=None, label=None, color=None, marker=’o’,    scatter_kws=None, line_kws=None, ax=None)

Parameters: The description of some main parameters are given below:

  • x, y: These are Input variables. If strings, these should correspond with column names in “data”. When pandas objects are used, axes will be labeled with the series name.
  • data:  This is dataframe where each column is a variable and each row is an observation.
  • lowess: (optional) This parameter take boolean value. If “True”, use “statsmodels” to estimate a nonparametric lowess model (locally weighted linear regression).
  • color: (optional) Color to apply to all plot elements.
  • marker: (optional) Marker to use for the scatterplot glyphs.

Return: The Axes object containing the plot.

Below is the implementation of above method:

Example 1:

Python3




# importing required packages
import seaborn as sns
import matplotlib.pyplot as plt
  
# loading dataset
data = sns.load_dataset("mpg")
  
# draw regplot
sns.regplot(x = "mpg"
            y = "acceleration"
            data = data)
  
# show the plot
plt.show()
  
# This code is contributed 
# by Deepanshu Rustagi.

Output :

Example 2:

Python3




# importing required packages
import seaborn as sns
import matplotlib.pyplot as plt
  
# loading dataset
data = sns.load_dataset("titanic")
  
# draw regplot
sns.regplot(x = "age",
            y = "fare",
            data = data,
            dropna = True)
# show the plot
plt.show()
  
# This code is contributed 
# by Deepanshu Rustagi.

Output :

Example 3:

Python3




# importing required packages
import seaborn as sns
import matplotlib.pyplot as plt
  
# loading dataset
data = sns.load_dataset("exercise")
  
# draw regplot
sns.regplot(x = "id",
            y = "pulse"
            data = data)
  
# show the plot
plt.show()
  
# This code is contributed 
# by Deepanshu Rustagi.

Output :

Example 4 :

Python3




# importing required packages
import seaborn as sns
import matplotlib.pyplot as plt
  
# loading dataset
data = sns.load_dataset("attention")
  
# draw regplot
sns.regplot(x = "solutions",
            y = "score",
            data = data)
  
# show ther plot
plt.show()
  
# This code is contributed 
# by Deepanshu Rustagi.

Output :


My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!