学ぶ Working with Legend

セクション 1. 章 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 left'`	`'upper center'`	`'upper right'`
`'center left'`	`'center'`	`'center right'`
`'lower left'`	`'lower center'`	`'lower right'`

You can also use the 'best' option, which automatically places the legend in the location with the least overlap with the plotted data. Additionally, using 'right' produces the same result as 'center right'.

Study More

You can explore more in legend() documentation

タスク

スワイプしてコーディングを開始

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.

解答

実践的な練習のためにデスクトップに切り替える下記のオプションのいずれかを利用して、現在の場所から続行する

すべて明確でしたか？

フィードバックありがとうございます！

セクション 1. 章 10

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