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.
- 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:
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