Why legends and colorbars guide reading in Matplotlib - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this matplotlib legend code?

Look at the code below that plots two lines with a legend. What will the legend show?

Matplotlib

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 5, 6], label='Line A')
plt.plot([1, 2, 3], [6, 5, 4], label='Line B')
plt.legend()
legend_texts = [text.get_text() for text in plt.gca().get_legend().get_texts()]
print(legend_texts)
plt.close()  # Prevent showing plot in test environment

A ['Line B', 'Line A']

B ['Line A', 'Line B']

C ['line a', 'line b']

D []

💡 Hint

Check the order of plotting and the labels assigned.

The legend shows labels in the order the lines were plotted. The first line is labeled 'Line A', the second 'Line B'.

❓ data_output

intermediate

How many colorbar ticks are shown in this heatmap?

This code creates a heatmap with a colorbar. How many ticks will the colorbar display?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(5,5)
fig, ax = plt.subplots()
cax = ax.imshow(data, cmap='viridis')
cb = fig.colorbar(cax, ticks=[0, 0.5, 1])
plt.close()
tick_labels = [tick.get_text() for tick in cb.ax.get_yticklabels() if tick.get_text() != '']
print(len(tick_labels))

A 3

B 5

C 0

D 1

💡 Hint

Look at the ticks argument passed to the colorbar.

The colorbar ticks are explicitly set to three values: 0, 0.5, and 1, so three ticks appear.

❓ visualization

advanced

Which option correctly adds a legend and colorbar to guide reading?

Given a scatter plot with points colored by a value, which code snippet correctly adds both a legend for categories and a colorbar for the color scale?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(5)
y = np.random.rand(5)
colors = np.linspace(0, 1, 5)
categories = ['A', 'B', 'A', 'B', 'A']

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=colors, cmap='plasma')

A ax.legend(categories)\nfig.colorbar(scatter)

B ax.scatter(x, y, label=categories)\nfig.colorbar(scatter)

C for cat in set(categories):\n ax.scatter([], [], label=cat)\nax.legend()\nfig.colorbar(scatter)

D ax.legend()\nfig.colorbar()

💡 Hint

Think about how to create legend entries for categories without plotting extra points.

Option C creates invisible scatter points for each category to add to the legend, then adds the colorbar for the color scale.

🔧 Debug

advanced

What error does this code raise when adding a colorbar?

Consider this code snippet that tries to add a colorbar but raises an error. What is the error?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
scatter = ax.scatter([1,2,3], [4,5,6])
fig.colorbar()
plt.close()

A AttributeError: 'NoneType' object has no attribute 'colorbar'

B No error, colorbar added successfully

C ValueError: Invalid colorbar parameters

D TypeError: colorbar() missing 1 required positional argument: 'mappable'

💡 Hint

Check the required arguments for fig.colorbar()

The colorbar function requires a mappable object (like the scatter) to know what to show. Omitting it causes a TypeError.

🚀 Application

expert

How does adding legends and colorbars improve data reading in plots?

Which statement best explains why legends and colorbars help people understand plots better?

A They provide labels and scales that connect colors and symbols to data meaning, making interpretation easier.

B They decorate the plot with colors and shapes to make it look more attractive.

C They automatically fix errors in the data visualization code.

D They reduce the amount of data shown so the plot is less cluttered.

💡 Hint

Think about how a reader knows what colors or symbols represent.

Legends and colorbars act as guides that explain what colors and symbols mean, helping readers understand the data clearly.