Create 3D Plots with Error Bars in Python

Introduction

In this tutorial, we will learn how to create a 3D plot with error bars using Python's Matplotlib library. Error bars are a graphical representation of the variability of data and are often used in scientific and engineering fields to show uncertainties in measurements or statistical estimates.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

Import Libraries

First, we need to import the necessary libraries, which are Matplotlib and NumPy. NumPy is a numerical computing library that provides support for arrays and matrices, while Matplotlib is a data visualization library.

import matplotlib.pyplot as plt
import numpy as np

Create a 3D Plot

Next, we create a 3D plot by using the add_subplot method of the figure object. We set the projection parameter to '3d' to specify that we want a 3D plot.

ax = plt.figure().add_subplot(projection='3d')

Generate Data for the Plot

We generate the data for our plot by creating a parametric curve. A parametric curve is a set of equations that describe the x, y, and z coordinates as a function of a parameter. We use NumPy's arange function to create an array of values from 0 to 2π. We then use these values to calculate the x, y, and z coordinates using trigonometric functions.

t = np.arange(0, 2*np.pi+.1, 0.01)
x, y, z = np.sin(t), np.cos(3*t), np.sin(5*t)

Add Error Bars to the Plot

We add error bars to our plot using the errorbar method of the Axes3D object. We set the zuplims and zlolims parameters to arrays that specify which data points have upper and lower limits. We set the errorevery parameter to control the frequency of error bars.

estep = 15
i = np.arange(t.size)
zuplims = (i % estep == 0) & (i // estep % 3 == 0)
zlolims = (i % estep == 0) & (i // estep % 3 == 2)

ax.errorbar(x, y, z, 0.2, zuplims=zuplims, zlolims=zlolims, errorevery=estep)

Customize the Plot

We can customize our plot by adding labels to the x, y, and z axes using the set_xlabel, set_ylabel, and set_zlabel methods.

ax.set_xlabel("X label")
ax.set_ylabel("Y label")
ax.set_zlabel("Z label")

Display the Plot

Finally, we use the show method to display our plot.

plt.show()

Summary

In this tutorial, we learned how to create a 3D plot with error bars using Matplotlib. We used NumPy to generate data for our plot and added error bars using the errorbar method. We also customized our plot by adding labels to the x, y, and z axes.