Lernen Legend | Section

Abschnitt 1. Kapitel 10

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When a chart contains multiple elements, adding a legend helps clarify what each element represents. matplotlib offers several ways to create a legend.

First Option

You can define all labels directly inside plt.legend():


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import numpy as np
import matplotlib.pyplot as plt

questions = ['question_1', 'question_2', 'question_3']
yes_answers = np.array([500, 240, 726])
no_answers = np.array([432, 618, 101])
answers = np.array([yes_answers, no_answers])

positions = np.arange(len(questions))
width = 0.3

for i in range(len(answers)):
    plt.bar(positions + width * i, answers[i], width)

plt.xticks(positions + width*(len(answers)-1)/2, questions)

plt.legend(['positive answers', 'negative answers'])
plt.show()

This creates a legend in the upper-left corner by passing a list of labels into plt.legend().

Second Option

You can also assign labels directly inside plotting functions using the label= parameter:


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import matplotlib.pyplot as plt
import numpy as np

questions = ['question_1', 'question_2', 'question_3']
positions = np.arange(len(questions))

yes_answers = np.array([500, 240, 726])
no_answers = np.array([432, 618, 101])
answers = [yes_answers, no_answers]
labels = ['positive answers', 'negative answers']

width = 0.3

for i in range(len(answers)):
    plt.bar(positions + width*i, answers[i], width, label=labels[i])

plt.xticks(positions + width*(len(answers)-1)/2, questions)

plt.legend()
plt.show()

Here, plt.legend() automatically gathers labels from the plotted elements.

Third Option

You can also set labels using the set_label() method of the returned artist:


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import matplotlib.pyplot as plt
import numpy as np

questions = ['question_1', 'question_2', 'question_3']
positions = np.arange(len(questions))

yes_answers = np.array([500, 240, 726])
no_answers = np.array([432, 618, 101])
answers = [yes_answers, no_answers]

width = 0.3
labels = ['positive answers', 'negative answers']

for i in range(len(answers)):
    bar = plt.bar(positions + width*i, answers[i], width)
    bar.set_label(labels[i])

center_positions = positions + width*(len(answers)-1)/2
plt.xticks(center_positions, questions)

plt.legend(loc='upper center')
plt.show()

Legend Location

The loc argument controls where the legend appears. The default 'best' asks matplotlib to choose an optimal location automatically.


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import matplotlib.pyplot as plt
import numpy as np

questions = ['question_1', 'question_2', 'question_3']
positions = np.arange(len(questions))

yes_answers = np.array([500, 240, 726])
no_answers = np.array([432, 618, 101])
answers = [yes_answers, no_answers]
labels = ['positive answers', 'negative answers']

width = 0.3

for i, label in enumerate(labels):
    bars = plt.bar(positions + width*i, answers[i], width)
    bars.set_label(label)

center_positions = positions + width*(len(answers)-1)/2
plt.xticks(center_positions, questions)

plt.legend(loc='upper center')
plt.show()

Valid values for loc include: 'upper right', 'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'center'.

Study More

You can explore more in legend() documentation

Aufgabe

Swipe to start coding

Label the lowest bars as 'primary sector' specifying the appropriate keyword argument.
Label the bars in the middle as 'secondary sector' specifying the appropriate keyword argument.
Label the top bars as 'tertiary sector' specifying the appropriate keyword argument.
Place the legend on the right side, centered vertically.

Lösung

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden

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Abschnitt 1. Kapitel 10

single

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