Customizing Contour Levels in Kde Plot
Customizing contour levels can help in emphasizing specific aspects of the data distribution. For example, if you are interested in the most densely populated regions of your data, you can set higher contour levels:
sns.kdeplot(x=data[:, 0], y=data[:, 1], levels=[0.7, 0.8, 0.9], cmap="Reds")
plt.show()
Output:
Customizing Contour Levels
In this example, the KDE plot will focus on the regions where the data density is highest, using the “Reds” colormap.
What Does Levels Mean In Seaborn Kde Plot?
Seaborn is a powerful Python visualization library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics. One of the many useful functions in Seaborn is kdeplot, which stands for Kernel Density Estimate plot. This function is used to visualize the probability density of a continuous variable. When dealing with bivariate data, kdeplot can also generate contour plots, which are particularly useful for understanding the distribution of data points in a two-dimensional space.
In this article, we will delve into the concept of “levels” in Seaborn’s kdeplot function. We will explore what levels mean, how to use them, and their practical applications in data visualization.
Table of Content
- Understanding Kernel Density Estimation (KDE)
- What Does levels Mean in Seaborn KDE Plot?
- Levels Parameter in a Seaborn KDE plot : Implementation
- Understanding Iso-Proportions of Density in KDE Plots
- Customizing Contour Levels in Kde Plot
- Comparing Multiple Datasets Using Kde Plot