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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()

Output: Example 1

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()

Output: Example 2

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