Error bars on bar charts in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of bar chart with error bars

What will be the output of this code snippet that plots a bar chart with error bars?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = ['A', 'B', 'C']
values = [5, 7, 3]
errors = [0.5, 1.0, 0.3]

plt.bar(x, values, yerr=errors, capsize=5)
plt.show()

A A bar chart with three bars labeled A, B, C, each with vertical error bars of lengths 0.5, 1.0, and 0.3 respectively, and caps on error bars.

B A line chart connecting points A, B, C with error bars.

C A bar chart with three bars but no error bars shown.

D A scatter plot with error bars but no bars.

💡 Hint

Look at the parameters yerr and capsize in plt.bar.

The plt.bar function with yerr parameter adds vertical error bars to each bar. The capsize parameter adds horizontal caps to the error bars.

❓ data_output

intermediate

Resulting error bar lengths in bar chart

Given the following data and error arrays, what are the lengths of the error bars shown on the bar chart?

Matplotlib

values = [10, 15, 8]
errors = [2, 3, 1]
# These are used as yerr in plt.bar

A [2, 3, 1]

B [10, 15, 8]

C [12, 18, 9]

D [1, 2, 3]

💡 Hint

yerr parameter controls the error bar lengths.

The error bars lengths correspond exactly to the values passed in the yerr parameter, which is the errors array [2, 3, 1].

🔧 Debug

advanced

Identify the error in error bar plotting code

What error will this code raise when trying to plot error bars on a bar chart?

Matplotlib

import matplotlib.pyplot as plt

x = ['X', 'Y', 'Z']
values = [4, 6, 5]
errors = [0.2, 0.3]

plt.bar(x, values, yerr=errors)
plt.show()

A TypeError: yerr must be a scalar or array of same length as values

B ValueError: shape mismatch between values and errors

C No error, plot shows correctly

D IndexError: list index out of range

💡 Hint

Check if the length of errors matches the length of values.

The errors array has length 2 but values has length 3. Matplotlib raises a ValueError due to shape mismatch when yerr length does not match values length.

❓ visualization

advanced

Effect of capsize parameter on error bars

Which option best describes the visual difference when changing the capsize parameter in plt.bar with error bars?

Matplotlib

import matplotlib.pyplot as plt

x = ['P', 'Q', 'R']
values = [7, 9, 6]
errors = [0.5, 0.7, 0.4]

plt.bar(x, values, yerr=errors, capsize=10)
plt.show()

A Error bars are removed completely.

B Error bars are thicker but have no caps.

C Error bars are colored red instead of black.

D Error bars have horizontal caps at the ends, making them look like a 'T' shape with length 10 pixels.

💡 Hint

capsize controls the width of the horizontal line at the top of error bars.

The capsize parameter adds horizontal caps to the error bars. A larger capsize value makes the caps longer.

🚀 Application

expert

Calculate and plot error bars from raw data

You have three groups of measurements: group1 = [5, 6, 7], group2 = [8, 9, 10], group3 = [4, 5, 6]. Calculate the mean and standard deviation for each group, then plot a bar chart with error bars representing the standard deviation. Which code snippet correctly performs this task?

A

import matplotlib.pyplot as plt
import numpy as np

groups = [ [5,6,7], [8,9,10], [4,5,6] ]
means = [np.mean(g) for g in groups]
stds = [np.std(g, ddof=0) for g in groups]
labels = ['G1', 'G2', 'G3']
plt.bar(labels, means, yerr=stds, capsize=5)
plt.show()

B

import matplotlib.pyplot as plt
import numpy as np

groups = [ [5,6,7], [8,9,10], [4,5,6] ]
means = [np.mean(g) for g in groups]
stds = [np.std(g) for g in groups]
labels = ['G1', 'G2', 'G3']
plt.bar(labels, means, yerr=stds)
plt.show()

C

import matplotlib.pyplot as plt
import numpy as np

groups = [ [5,6,7], [8,9,10], [4,5,6] ]
means = [np.mean(g) for g in groups]
stds = [np.std(g, ddof=1) for g in groups]
labels = ['G1', 'G2', 'G3']
plt.bar(labels, means, yerr=stds, capsize=5)
plt.show()

D

import matplotlib.pyplot as plt
import numpy as np

groups = [ [5,6,7], [8,9,10], [4,5,6] ]
means = [np.mean(g) for g in groups]
stds = [np.std(g, ddof=2) for g in groups]
labels = ['G1', 'G2', 'G3']
plt.bar(labels, means, yerr=stds, capsize=5)
plt.show()

💡 Hint

Use sample standard deviation (ddof=1) for error bars representing variability.

The sample standard deviation uses ddof=1. Option C correctly calculates means and sample std deviations, then plots bars with error bars and caps.