Python 如何使条形图居中以显示某一列的差异？

如何使条形图居中以显示某一列的差异

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

我使用matplotlib绘制了以下条形图：

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

请注意条形图是多么糟糕。每个小节之间的差异无法真正正确地看到。所以我想要的是，使用红色条作为y轴的原点。这样，其他条将显示蓝色_bari-redbar的差异

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

换句话说，我希望y轴上红色条的值是绘图的y原点

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

换言之，红色条是我的学术工作所获得的准确度。我想画出与我相比的其他文章结果

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

我使用paint.net制作了以下图片来说明我想要的内容。 非常感谢您的任何其他想法/建议

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

附件:

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

我使用以下代码生成第一个图形：

import numpy as np
import random
from matplotlib import pyplot as plt

accuracies = [0.9630, 0.9597, 0.9563, 0.9533, 0.9527, 0.9480, 0.9477, 0.9472, 0.9472, 0.9466, 0.9452, 0.9452, 0.9442,
              0.9440, 0.9434, 0.9420, 0.9407, 0.9407, 0.9391, 0.9377, 0.9185, 0.9268]

sensitividades = [0.7680, 0.7200, 0.8173, 0.7569, 0.7406, 0.7354, 0.7746, 0.7344, 0.7067, 0.7410, 0.7370, 0.7321,
                  0.7357]

especificidades = [0.9827, 0.9733, 0.9816, 0.9807, 0.9789, 0.9724, 0.9764, 0.9801, 0.9751, 0.9521, 0.9487, 0.9694]

accuracies = [x * 100 for x in accuracies]

y = accuracies

N = len(y)
x = range(N)
width = 1 / 1.1
fig, ax = plt.subplots(1, 1)
ax.grid(zorder=0)

# Plot other articles
ax.bar(x, y, width, color="blue", zorder=3)

# Plot my work
ax.bar(x[len(x) - 1] + 1, 95.30, width, color="red", zorder=3)

plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.savefig('foo.png')
plt.show()

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

---

事实上，我认为你现在表达它的方式实际上是最好的——这意味着在粗略的水平上准确度没有很大的差别

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

但是，如果要将红色条的值设置为原点，请尝试以下操作：

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(95.30) # Sets the value of the red bar as the origin.

plt.savefig('foo.png')
plt.show()

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

也许设置文章的最低准确度的最小值可能会使这个图更容易理解

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

---

事实上，我认为你现在表达它的方式实际上是最好的——这意味着在粗略的水平上准确度没有很大的差别

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

但是，如果要将红色条的值设置为原点，请尝试以下操作：

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(95.30) # Sets the value of the red bar as the origin.

plt.savefig('foo.png')
plt.show()

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

也许设置文章的最低准确度的最小值可能会使这个图更容易理解

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

---

您可以将y限制设置为更接近感兴趣的值：

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

import numpy as np
import random
from matplotlib import pyplot as plt

accuracies = [0.9630, 0.9597, 0.9563, 0.9533, 0.9527, 0.9480, 0.9477, 0.9472, 0.9472, 0.9466, 0.9452, 0.9452, 0.9442,
              0.9440, 0.9434, 0.9420, 0.9407, 0.9407, 0.9391, 0.9377, 0.9185, 0.9268]

sensitividades = [0.7680, 0.7200, 0.8173, 0.7569, 0.7406, 0.7354, 0.7746, 0.7344, 0.7067, 0.7410, 0.7370, 0.7321,
                  0.7357]

especificidades = [0.9827, 0.9733, 0.9816, 0.9807, 0.9789, 0.9724, 0.9764, 0.9801, 0.9751, 0.9521, 0.9487, 0.9694]

accuracies = [x * 100 for x in accuracies]

my_acc = 95.30
y = accuracies

N = len(y)
x = range(N)
width = 1 / 1.1
fig, ax = plt.subplots(1, 1)
ax.grid(zorder=0)

# Plot other articles
ax.bar(x, y, width, color="blue", zorder=3)

# Plot my work
ax.bar(x[len(x) - 1] + 1, my_acc, width, color="red", zorder=3)

plt.title('Accuracy of each article')
plt.ylim(min(y) - 0.5, max(y) +0.5)
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.savefig('foo2.png')
plt.show()

或者，您可以在零附近绘制它，结果是新原点，但您必须通过在图例中或其他地方移动原点的程度来指示：

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

import numpy as np
import random
from matplotlib import pyplot as plt

accuracies = [0.9630, 0.9597, 0.9563, 0.9533, 0.9527, 0.9480, 0.9477, 0.9472, 0.9472, 0.9466, 0.9452, 0.9452, 0.9442,
              0.9440, 0.9434, 0.9420, 0.9407, 0.9407, 0.9391, 0.9377, 0.9185, 0.9268]

sensitividades = [0.7680, 0.7200, 0.8173, 0.7569, 0.7406, 0.7354, 0.7746, 0.7344, 0.7067, 0.7410, 0.7370, 0.7321,
                  0.7357]

especificidades = [0.9827, 0.9733, 0.9816, 0.9807, 0.9789, 0.9724, 0.9764, 0.9801, 0.9751, 0.9521, 0.9487, 0.9694]

accuracies = [x * 100 for x in accuracies]

my_acc = 95.30
y = np.asarray(accuracies) - my_acc

N = len(y)
x = range(N)
width = 1 / 1.1
fig, ax = plt.subplots(1, 1)
ax.grid(zorder=0)

# Plot other articles
bars = ax.bar(x, y, width, color="blue", zorder=3)

# Plot my work
# ax.bar(x[len(x) - 1] + 1, my_acc, width, color="red", zorder=3)

plt.title('Accuracy of each article')
plt.yticks([0, -0.3, -1.3, -2.3, -3.3, 0.7, 1.7], [95.30, 95, 94, 93, 92, 96, 97])
plt.xlabel('Article')
plt.ylabel('Accuracy')
plt.ylim(min(y) - 0.5, max(y) + 0.7)


def autolabel(rects):
    for i in range(len(rects)):
        rect = rects[i]
        height = rect.get_height()
        if (height >= 0):
            ax.text(rect.get_x() + rect.get_width()/2.,
            0.3 + height,'[{}]'.format( i), ha='center', va='bottom', 
            fontsize=7.5)
        if (height < 0):
            ax.text(rect.get_x() + rect.get_width()/2.,
            height - 0.3,'[{}]'.format( i), ha='center', va='bottom', 
            fontsize=7.5)

autolabel(bars)
plt.savefig('foo.png')
plt.show()

---

当然，您自己的结果不会出现在第二个绘图中，因为它的高度为零。

您可以将y限制设置为更接近感兴趣的值：

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

import numpy as np
import random
from matplotlib import pyplot as plt

accuracies = [0.9630, 0.9597, 0.9563, 0.9533, 0.9527, 0.9480, 0.9477, 0.9472, 0.9472, 0.9466, 0.9452, 0.9452, 0.9442,
              0.9440, 0.9434, 0.9420, 0.9407, 0.9407, 0.9391, 0.9377, 0.9185, 0.9268]

sensitividades = [0.7680, 0.7200, 0.8173, 0.7569, 0.7406, 0.7354, 0.7746, 0.7344, 0.7067, 0.7410, 0.7370, 0.7321,
                  0.7357]

especificidades = [0.9827, 0.9733, 0.9816, 0.9807, 0.9789, 0.9724, 0.9764, 0.9801, 0.9751, 0.9521, 0.9487, 0.9694]

accuracies = [x * 100 for x in accuracies]

my_acc = 95.30
y = accuracies

N = len(y)
x = range(N)
width = 1 / 1.1
fig, ax = plt.subplots(1, 1)
ax.grid(zorder=0)

# Plot other articles
ax.bar(x, y, width, color="blue", zorder=3)

# Plot my work
ax.bar(x[len(x) - 1] + 1, my_acc, width, color="red", zorder=3)

plt.title('Accuracy of each article')
plt.ylim(min(y) - 0.5, max(y) +0.5)
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.savefig('foo2.png')
plt.show()

或者，您可以在零附近绘制它，结果是新原点，但您必须通过在图例中或其他地方移动原点的程度来指示：

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()

import numpy as np
import random
from matplotlib import pyplot as plt

accuracies = [0.9630, 0.9597, 0.9563, 0.9533, 0.9527, 0.9480, 0.9477, 0.9472, 0.9472, 0.9466, 0.9452, 0.9452, 0.9442,
              0.9440, 0.9434, 0.9420, 0.9407, 0.9407, 0.9391, 0.9377, 0.9185, 0.9268]

sensitividades = [0.7680, 0.7200, 0.8173, 0.7569, 0.7406, 0.7354, 0.7746, 0.7344, 0.7067, 0.7410, 0.7370, 0.7321,
                  0.7357]

especificidades = [0.9827, 0.9733, 0.9816, 0.9807, 0.9789, 0.9724, 0.9764, 0.9801, 0.9751, 0.9521, 0.9487, 0.9694]

accuracies = [x * 100 for x in accuracies]

my_acc = 95.30
y = np.asarray(accuracies) - my_acc

N = len(y)
x = range(N)
width = 1 / 1.1
fig, ax = plt.subplots(1, 1)
ax.grid(zorder=0)

# Plot other articles
bars = ax.bar(x, y, width, color="blue", zorder=3)

# Plot my work
# ax.bar(x[len(x) - 1] + 1, my_acc, width, color="red", zorder=3)

plt.title('Accuracy of each article')
plt.yticks([0, -0.3, -1.3, -2.3, -3.3, 0.7, 1.7], [95.30, 95, 94, 93, 92, 96, 97])
plt.xlabel('Article')
plt.ylabel('Accuracy')
plt.ylim(min(y) - 0.5, max(y) + 0.7)


def autolabel(rects):
    for i in range(len(rects)):
        rect = rects[i]
        height = rect.get_height()
        if (height >= 0):
            ax.text(rect.get_x() + rect.get_width()/2.,
            0.3 + height,'[{}]'.format( i), ha='center', va='bottom', 
            fontsize=7.5)
        if (height < 0):
            ax.text(rect.get_x() + rect.get_width()/2.,
            height - 0.3,'[{}]'.format( i), ha='center', va='bottom', 
            fontsize=7.5)

autolabel(bars)
plt.savefig('foo.png')
plt.show()

---

当然，您自己的结果不会出现在第二个绘图中，因为它的高度为零。

Awesome，是否有办法固定y轴标签以正确指示精度差异？此外，如果可能的话，我如何向每个酒吧添加一个标签Mr et al.，Bla Bla et al。。。。我修改了答案以显示正确的y标签。关于你的第二个问题：你想让其他作者的一段文字显示在每个条的上方吗？在这种情况下，可能会有相当多的酒吧以上混乱，因为这将是大量的文本。您可以在条形图上方写“[1]”、“[2]”，并在文本中指出它们的来源。我也在更新的答案中包含了这一点。我对此进行了修改：太棒了，有没有办法修复y轴标签以正确指示精度差异？此外，如果可能的话，我如何向每个酒吧添加一个标签Mr et al.，Bla Bla et al。。。。我修改了答案以显示正确的y标签。关于你的第二个问题：你想让其他作者的一段文字显示在每个条的上方吗？在这种情况下，可能会有相当多的酒吧以上混乱，因为这将是大量的文本。您可以在条形图上方写“[1]”、“[2]”，并在文本中指出它们的来源。我也在更新的答案中包含了这一点。我用了一个修改：谢谢你的回复。然而，在我的情况下，上述答案更合适。但我很感激你的快速回复和努力：没问题：。我更喜欢数据的ml4294s表示。谢谢回复。然而，在我的情况下，上述答案更合适。但我很感激你的快速回复和努力：没问题：。我更喜欢数据的ml4294s表示。

...
plt.title('Accuracy of each article')
plt.xlabel('Article')
plt.ylabel('Accuracy')

plt.ylim(min(accuracies), 100) # Sets the value of minimum accuracy as the origin and the max value as 100.

plt.savefig('foo.png')
plt.show()