Advanced Seaborn: Demystifying the Complex Plots!
Welcome back to the second installment of our two-part series of Seaborn. In part 1, We had an introduction of seaborn and we saw how we can create Categorical Plots and Univariate Plots. In this article, we delve into the advanced topics of Seaborn to unlock its true potential by creating BiVariate Plots, Multi-Variate Plots, and Matrix Plots.
Bivariate plots involve the visualization and analysis of the relationship between two variables simultaneously. They are used to explore how two variables are related or correlated. Common ones with Matplotlib are sns.scatterplot(x,y,data) , sns.lineplot(x,y,data) for scatter and line plots. Will see more about some uncommon plots here.
Regression Plot
A Regression Plot focuses on the relationship between two numerical variables: the independent variable (often on the x-axis) and the dependent variable (on the y-axis). There are individual data points are displayed as dots and the central element of a Regression Plot is the regression line or curve, which represents the best-fitting mathematical model that describes the relationship between the variables.
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