Contour Plot using Matplotlib - Python
Last Updated : 21 Apr, 2020
Contour plots also called level plots are a tool for doing multivariate analysis and visualizing 3-D plots in 2-D space. If we consider X and Y as our variables we want to plot then the response Z will be plotted as slices on the X-Y plane due to which contours are sometimes referred as
Z-slices or iso-response. Contour plots are widely used to visualize density, altitudes or heights of the mountain as well as in the meteorological department. Due to such wide usage
matplotlib.pyplot provides a method
contour to make it easy for us to draw contour plots.
matplotlib.pyplot.contour
The
matplotlib.pyplot.contour() are usually useful when
Z = f(X, Y) i.e Z changes as a function of input X and Y. A
contourf() is also available which allows us to draw filled contours.
Syntax: matplotlib.pyplot.contour([X, Y, ] Z, [levels], **kwargs) Parameters: X, Y: 2-D numpy arrays with same shape as Z or 1-D arrays such that len(X)==M and len(Y)==N (where M and N are rows and columns of Z) Z: The height values over which the contour is drawn. Shape is (M, N) levels: Determines the number and positions of the contour lines / regions. Returns: QuadContourSet
Below examples illustrate the
matplotlib.pyplot.contour() function in matplotlib.pyplot:
Example #1: Plotting of Contour using
contour() which only plots contour lines.
Python3 1== # Implementation of matplotlib function import matplotlib.pyplot as plt import numpy as np feature_x = np.arange(0, 50, 2) feature_y = np.arange(0, 50, 3) # Creating 2-D grid of features [X, Y] = np.meshgrid(feature_x, feature_y) fig, ax = plt.subplots(1, 1) Z = np.cos(X / 2) + np.sin(Y / 4) # plots contour lines ax.contour(X, Y, Z) ax.set_title('Contour Plot') ax.set_xlabel('feature_x') ax.set_ylabel('feature_y') plt.show() 
Example #2: Plotting of contour using
contourf() which plots filled contours.
Python3 1== # Implementation of matplotlib function import matplotlib.pyplot as plt import numpy as np feature_x = np.linspace(-5.0, 3.0, 70) feature_y = np.linspace(-5.0, 3.0, 70) # Creating 2-D grid of features [X, Y] = np.meshgrid(feature_x, feature_y) fig, ax = plt.subplots(1, 1) Z = X ** 2 + Y ** 2 # plots filled contour plot ax.contourf(X, Y, Z) ax.set_title('Filled Contour Plot') ax.set_xlabel('feature_x') ax.set_ylabel('feature_y') plt.show() 
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