Python - seaborn.PairGrid() method
Last Updated :
31 Mar, 2023
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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 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:
# 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:
# 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:
