Creating a Simple Contour Plot
The kdeplot() function in Seaborn is used to create contour plots. You need to provide two numerical variables as input, one for each axis. The function will calculate the kernel density estimate and represent it as a contour plot.
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# Generate sample data
np.random.seed(0)
x = np.random.randn(1000)
y = np.random.randn(1000)
# Create a contour plot
plt.figure(figsize=(6, 6))
sns.kdeplot(x=x, y=y, fill=True, cmap="viridis", thresh=0, levels=100)
plt.title('Contour Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()
Output:
Basic Contour plot
Mastering Contour Plots with Seaborn
Contour plots, also known as density plots, are a graphical method to visualize the 3-D surface by plotting constant Z slices called contours in a 2-D format. Seaborn, a Python data visualization library based on Matplotlib, provides a convenient way to create contour plots using the kdeplot() function. This article will guide you through the process of creating and customizing contour plots using Seaborn.
Table of Content
- Introduction to Contour Plots
- Creating a Simple Contour Plot
- Customizing Contour Plots Using Seaborn
- 1. Change Color Map (cmap):
- 2. Adjusting the Levels
- 3. Setting the Threshold
- 4. Removing Fill for Line-Only Plots
- 5. Modifying Line Width (linewidths)
- 6. Adding Gridlines