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Python – seaborn.PairGrid() method

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  • Difficulty Level : Hard
  • Last Updated : 15 Jul, 2020
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Prerequisite: Seaborn Programming Basics

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.PairGrid() :

  • Subplot grid for plotting pairwise relationships in a dataset.
  • This class maps each variable in a dataset onto a column and row in a grid of multiple axes. Different axes-level plotting functions can be used to draw bivariate plots in the upper and lower triangles, and the the marginal distribution of each variable can be shown on the diagonal.
  • It can also represent an additional level of conditionalization with the hue parameter, which plots different subsets of data in different colors. This uses color to resolve elements on a third dimension, but only draws subsets on top of each other and will not tailor the hue parameter for the specific visualization the way that axes-level functions that accept hue will.
                    seaborn.PairGrid( data, \*\*kwargs)

Seaborn.PairGrid uses many arguments as input, main of which are described below in form of table:

Arguments       Description                                                                                                                                 Value
data Tidy (long-form) dataframe where each column is a variable and each row is an observation. DataFrame
hue Variable in “data“ to map plot aspects to different colors. string (variable name), optional
palette Set of colors for mapping the “hue“ variable. If a dict, keys should be values  in the “hue“ variable. dict or seaborn color palette
vars  Variables within “data“ to use, otherwise use every column with a numeric datatype. list of variable names, optional
dropna Drop missing values from the data before plotting. boolean, optional

Below is the implementation of above method:

Example 1:

Python3




# importing packages
import seaborn
import matplotlib.pyplot as plt
  
# loading dataset
df = seaborn.load_dataset('tips')
  
# PairGrid object with hue
graph = seaborn.PairGrid(df, hue ='day')
# type of graph for diagonal
graph = graph.map_diag(plt.hist)
# type of graph for non-diagonal
graph = graph.map_offdiag(plt.scatter)
# to add legends
graph = graph.add_legend()
# to show
plt.show()
# This code is contributed by Deepanshu Rusatgi.


Output :

Example 2:

Python3




# importing packages
import seaborn
import matplotlib.pyplot as plt
  
# loading dataset
df = seaborn.load_dataset('tips')
  
# PairGrid object with hue
graph = seaborn.PairGrid(df)
# type of graph for non-diagonal(upper part)
graph = graph.map_upper(sns.scatterplot)
# type of graph for non-diagonal(lower part)
graph = graph.map_lower(sns.kdeplot)
# type of graph for diagonal
graph = graph.map_diag(sns.kdeplot, lw = 2)
# to show
plt.show()
# This code is contributed by Deepanshu Rusatgi.


Output:


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