Matplotlib Error Bar Customization

Introduction

In data visualization, error bars are a useful tool to display the uncertainty in the data. Error bars are graphical representations of the variability of data and are used on graphs to indicate the error or uncertainty in a reported measurement. In this lab, we will learn about the different ways of specifying error bars in Matplotlib.

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

We will begin by importing the necessary libraries, including Matplotlib and NumPy.

import matplotlib.pyplot as plt
import numpy as np

Define Data

Next, we will define our x and y data. In this example, we will use the np.arange() and np.exp() functions to create x and y data, respectively.

## example data
x = np.arange(0.1, 4, 0.5)
y = np.exp(-x)

Define Error Values

We will now define our error values. In this example, we will use the error variable to represent symmetric error and the asymmetric_error variable to represent asymmetric error.

## example error bar values that vary with x-position
error = 0.1 + 0.2 * x

## error bar values w/ different -/+ errors that
## also vary with the x-position
lower_error = 0.4 * error
upper_error = error
asymmetric_error = [lower_error, upper_error]

Plot Variable, Symmetric Error Bars

We will now plot our data with variable, symmetric error bars. The ax.errorbar() function is used to create the plot, and the yerr parameter is used to specify the error values.

## plot variable, symmetric error bars
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr=error, fmt='-o')
ax.set_title('Variable, Symmetric Error Bars')
plt.show()

Plot Variable, Asymmetric Error Bars

Next, we will plot our data with variable, asymmetric error bars. The ax.errorbar() function is used again, but this time the xerr parameter is used to specify the asymmetric error values.

## plot variable, asymmetric error bars
fig, ax = plt.subplots()
ax.errorbar(x, y, xerr=asymmetric_error, fmt='o')
ax.set_title('Variable, Asymmetric Error Bars')
plt.show()

Plot Log Scale with Error Bars

Finally, we will plot our data with a log scale and error bars. The ax.set_yscale() function is used to set the y-axis to a logarithmic scale.

## plot log scale with error bars
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr=error, fmt='o')
ax.set_title('Log Scale with Error Bars')
ax.set_yscale('log')
plt.show()

Summary

In this lab, we learned about the different ways of specifying error bars in Matplotlib. We started by importing the necessary libraries and defining our data and error values. We then created plots with variable, symmetric error bars and variable, asymmetric error bars. Finally, we plotted our data with a logarithmic scale and error bars. By using error bars in our visualizations, we can provide valuable information about the uncertainty in the data.