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Python – seaborn.PairGrid() method

  • Last Updated : 15 Jul, 2020

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
dataTidy (long-form) dataframe where each column is a variable and each row is an observation.DataFrame
hueVariable in “data“ to map plot aspects to different colors.string (variable name), optional
paletteSet 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
dropnaDrop 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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