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:
|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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