violinplot 中重复的 y 轴刻度
repeated y axis ticks in violinplot
我想用 violinplot 绘制一组 1 到 800 之间的值的分布，我已经使用了这段代码。我对此很陌生。
import matplotlib.pyplot as plt
from matplotlib import ticker as mticker
import seaborn as sns
import numpy as np
log_data = [[np.log10(d) for d in row] for row in [data['count']]]
print(log_data)
fig, ax = plt.subplots()
sns.violinplot(data=log_data, ax=ax)
plt.show()
为什么我有三个 10^0？
这是我的数据：
[ 8, 7, 5, 1, 2, 6, 5, 1, 2, 31, 9, 40, 9,
53, 4, 8, 3, 1, 46, 2, 18, 4, 17, 26, 17, 2,
19, 14, 2, 16, 35, 42, 22, 2, 19, 13, 59, 11, 69,
33, 2, 2, 24, 86, 16, 11, 7, 5, 18, 22, 1, 2,
16, 28, 3, 2, 12, 16, 1, 8, 1, 2, 5, 4, 9,
1, 1, 5, 1, 4, 5, 2, 11, 25, 6, 45, 64, 6,
2, 63, 26, 2, 3, 8, 3, 16, 8, 2, 2, 99, 2,
51, 43, 5, 53, 10, 19, 20, 6, 9, 1, 4, 1, 19,
4, 2, 3, 2, 77, 4, 7, 3, 2, 1, 81, 15, 50,
22, 58, 21, 10, 1, 18, 8, 1, 35, 2, 32, 18, 12,
11, 7, 5, 27, 29, 1, 2, 5, 1, 2, 3, 3, 1,
45, 22, 1, 12, 2, 21, 4, 1, 19, 27, 23, 3, 1,
21, 1, 124, 13, 17, 1, 18, 33, 23, 3, 6, 2, 8,
3, 1, 228, 28, 1, 1, 122, 868, 47, 2, 1, 9, 108,
10, 1, 5, 40, 43, 5, 2, 137, 9, 11, 19, 19, 11,
21, 8, 1, 6, 2, 3, 3, 26, 42, 14, 1, 14, 15,
3, 30, 17, 5, 17, 3, 38, 11, 54, 3, 1, 1, 3,
3, 7, 3, 1, 1, 5, 9, 1, 5, 4, 7, 35, 8,
10, 6, 6, 5, 3, 28, 2, 2, 5, 13, 6, 2, 4,
3, 2, 7, 52, 31, 1, 7, 7, 216, 4, 13, 6, 14,
4, 4, 5, 102, 3, 15, 4, 12, 48, 5, 9, 3, 10,
35, 36, 2, 10, 2, 55, 15, 17, 2, 19, 14, 14, 15,
5, 4, 11, 1, 1, 18, 4, 63, 63, 22, 37, 2, 22,
8, 22, 8, 20, 104, 3, 2, 6, 11, 20, 1, 3, 78,
2, 1, 52, 33, 2, 4, 9, 1, 27, 9, 4, 4, 2,
9, 9, 2, 24, 137, 12, 2, 2, 1, 6, 11, 8, 1,
20, 23, 75, 5, 1, 14, 3, 31, 15, 4, 2, 26, 50,
9, 75, 42, 14, 4, 1, 2, 9, 34, 25, 37, 53, 122,
28, 52, 22, 1, 109, 1, 1, 11, 1, 15, 2, 9, 32,
23, 5, 6, 3, 2, 51, 9, 12, 10, 7, 5, 2, 1,
311, 41, 1, 6, 13, 2, 5, 18, 105, 13, 17, 3, 9,
48, 2, 15, 18, 16, 77, 13, 3, 2, 2, 8, 1, 3,
4, 93, 23, 169, 1, 24, 2, 1, 8, 36, 1, 1, 1,
6, 3, 1, 25, 1, 2, 59, 2, 3, 3, 1, 8, 2,
1, 6, 15, 1, 7, 29, 4, 4, 8, 22, 5, 80, 16,
3、147、23、6、16、1、8、530]
使用 set_yscale
ax.set_yscale('log')
sns.violinplot(data=first_issues_count, ax=ax)
小提琴图的对数刻度选项位于 roadmap for seaborn 0.12. Meanwhile, you can calculate the violinplot using the log10 of the _data and some formatting tricks, similar to 。
下面的示例代码显示了如何根据您的情况调整格式化技巧。为了比较，加了一个sns.boxenplot，用真实的对数刻度没有问题。
import matplotlib.pyplot as plt
from matplotlib.ticker import StrMethodFormatter
import seaborn as sns
import numpy as np
data = np.array([8, 7, 5, 1, 2, 6, 5, 1, 2, 31, 9, 40, 9, 53, 4, 8, 3, 1, 46, 2, 18, 4, 17, 26, 17, 2, 19, 14, 2, 16, 35, 42, 22, 2, 19, 13, 59, 11, 69, 33, 2, 2, 24, 86, 16, 11, 7, 5, 18, 22, 1, 2, 16, 28, 3, 2, 12, 16, 1, 8, 1, 2, 5, 4, 9, 1, 1, 5, 1, 4, 5, 2, 11, 25, 6, 45, 64, 6, 2, 63, 26, 2, 3, 8, 3, 16, 8, 2, 2, 99, 2, 51, 43, 5, 53, 10, 19, 20, 6, 9, 1, 4, 1, 19, 4, 2, 3, 2, 77, 4, 7, 3, 2, 1, 81, 15, 50, 22, 58, 21, 10, 1, 18, 8, 1, 35, 2, 32, 18, 12, 11, 7, 5, 27, 29, 1, 2, 5, 1, 2, 3, 3, 1, 45, 22, 1, 12, 2, 21, 4, 1, 19, 27, 23, 3, 1, 21, 1, 124, 13, 17, 1, 18, 33, 23, 3, 6, 2, 8, 3, 1, 228, 28, 1, 1, 122, 868, 47, 2, 1, 9, 108, 10, 1, 5, 40, 43, 5, 2, 137, 9, 11, 19, 19, 11, 21, 8, 1, 6, 2, 3, 3, 26, 42, 14, 1, 14, 15, 3, 30, 17, 5, 17, 3, 38, 11, 54, 3, 1, 1, 3, 3, 7, 3, 1, 1, 5, 9, 1, 5, 4, 7, 35, 8, 10, 6, 6, 5, 3, 28, 2, 2, 5, 13, 6, 2, 4, 3, 2, 7, 52, 31, 1, 7, 7, 216, 4, 13, 6, 14, 4, 4, 5, 102, 3, 15, 4, 12, 48, 5, 9, 3, 10, 35, 36, 2, 10, 2, 55, 15, 17, 2, 19, 14, 14, 15, 5, 4, 11, 1, 1, 18, 4, 63, 63, 22, 37, 2, 22, 8, 22, 8, 20, 104, 3, 2, 6, 11, 20, 1, 3, 78, 2, 1, 52, 33, 2, 4, 9, 1, 27, 9, 4, 4, 2, 9, 9, 2, 24, 137, 12, 2, 2, 1, 6, 11, 8, 1, 20, 23, 75, 5, 1, 14, 3, 31, 15, 4, 2, 26, 50, 9, 75, 42, 14, 4, 1, 2, 9, 34, 25, 37, 53, 122, 28, 52, 22, 1, 109, 1, 1, 11, 1, 15, 2, 9, 32, 23, 5, 6, 3, 2, 51, 9, 12, 10, 7, 5, 2, 1, 311, 41, 1, 6, 13, 2, 5, 18, 105, 13, 17, 3, 9, 48, 2, 15, 18, 16, 77, 13, 3, 2, 2, 8, 1, 3, 4, 93, 23, 169, 1, 24, 2, 1, 8, 36, 1, 1, 1, 6, 3, 1, 25, 1, 2, 59, 2, 3, 3, 1, 8, 2, 1, 6, 15, 1, 7, 29, 4, 4, 8, 22, 5, 80, 16, 3, 147, 23, 6, 16, 1, 8, 530])
sns.set_style('ticks')
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))
sns.violinplot(y=np.log10(data), ax=ax1)
major_ticks = np.arange(np.floor(np.log10(data).min()), np.log10(data).max() + 1)
ax1.yaxis.set_ticks(major_ticks, minor=False)
ax1.yaxis.set_ticks([np.log10(x) for p in major_ticks for x in np.linspace(10 ** p, 10 ** (p + 1), 10)], minor=True)
ax1.yaxis.set_major_formatter(StrMethodFormatter("^{{{x:.0f}}}$"))
ax2.set_yscale('log')
sns.boxenplot(y=data, ax=ax2)
ymin, ymax = ax1.get_ylim()
ax2.set_ylim(10**ymin, 10**ymax)
plt.tight_layout()
plt.show()
我想用 violinplot 绘制一组 1 到 800 之间的值的分布，我已经使用了这段代码。我对此很陌生。
import matplotlib.pyplot as plt
from matplotlib import ticker as mticker
import seaborn as sns
import numpy as np
log_data = [[np.log10(d) for d in row] for row in [data['count']]]
print(log_data)
fig, ax = plt.subplots()
sns.violinplot(data=log_data, ax=ax)
plt.show()
为什么我有三个 10^0？
这是我的数据： [ 8, 7, 5, 1, 2, 6, 5, 1, 2, 31, 9, 40, 9, 53, 4, 8, 3, 1, 46, 2, 18, 4, 17, 26, 17, 2, 19, 14, 2, 16, 35, 42, 22, 2, 19, 13, 59, 11, 69, 33, 2, 2, 24, 86, 16, 11, 7, 5, 18, 22, 1, 2, 16, 28, 3, 2, 12, 16, 1, 8, 1, 2, 5, 4, 9, 1, 1, 5, 1, 4, 5, 2, 11, 25, 6, 45, 64, 6, 2, 63, 26, 2, 3, 8, 3, 16, 8, 2, 2, 99, 2, 51, 43, 5, 53, 10, 19, 20, 6, 9, 1, 4, 1, 19, 4, 2, 3, 2, 77, 4, 7, 3, 2, 1, 81, 15, 50, 22, 58, 21, 10, 1, 18, 8, 1, 35, 2, 32, 18, 12, 11, 7, 5, 27, 29, 1, 2, 5, 1, 2, 3, 3, 1, 45, 22, 1, 12, 2, 21, 4, 1, 19, 27, 23, 3, 1, 21, 1, 124, 13, 17, 1, 18, 33, 23, 3, 6, 2, 8, 3, 1, 228, 28, 1, 1, 122, 868, 47, 2, 1, 9, 108, 10, 1, 5, 40, 43, 5, 2, 137, 9, 11, 19, 19, 11, 21, 8, 1, 6, 2, 3, 3, 26, 42, 14, 1, 14, 15, 3, 30, 17, 5, 17, 3, 38, 11, 54, 3, 1, 1, 3, 3, 7, 3, 1, 1, 5, 9, 1, 5, 4, 7, 35, 8, 10, 6, 6, 5, 3, 28, 2, 2, 5, 13, 6, 2, 4, 3, 2, 7, 52, 31, 1, 7, 7, 216, 4, 13, 6, 14, 4, 4, 5, 102, 3, 15, 4, 12, 48, 5, 9, 3, 10, 35, 36, 2, 10, 2, 55, 15, 17, 2, 19, 14, 14, 15, 5, 4, 11, 1, 1, 18, 4, 63, 63, 22, 37, 2, 22, 8, 22, 8, 20, 104, 3, 2, 6, 11, 20, 1, 3, 78, 2, 1, 52, 33, 2, 4, 9, 1, 27, 9, 4, 4, 2, 9, 9, 2, 24, 137, 12, 2, 2, 1, 6, 11, 8, 1, 20, 23, 75, 5, 1, 14, 3, 31, 15, 4, 2, 26, 50, 9, 75, 42, 14, 4, 1, 2, 9, 34, 25, 37, 53, 122, 28, 52, 22, 1, 109, 1, 1, 11, 1, 15, 2, 9, 32, 23, 5, 6, 3, 2, 51, 9, 12, 10, 7, 5, 2, 1, 311, 41, 1, 6, 13, 2, 5, 18, 105, 13, 17, 3, 9, 48, 2, 15, 18, 16, 77, 13, 3, 2, 2, 8, 1, 3, 4, 93, 23, 169, 1, 24, 2, 1, 8, 36, 1, 1, 1, 6, 3, 1, 25, 1, 2, 59, 2, 3, 3, 1, 8, 2, 1, 6, 15, 1, 7, 29, 4, 4, 8, 22, 5, 80, 16, 3、147、23、6、16、1、8、530]
使用 set_yscale
ax.set_yscale('log')
sns.violinplot(data=first_issues_count, ax=ax)
小提琴图的对数刻度选项位于 roadmap for seaborn 0.12. Meanwhile, you can calculate the violinplot using the log10 of the _data and some formatting tricks, similar to
下面的示例代码显示了如何根据您的情况调整格式化技巧。为了比较，加了一个sns.boxenplot，用真实的对数刻度没有问题。
import matplotlib.pyplot as plt
from matplotlib.ticker import StrMethodFormatter
import seaborn as sns
import numpy as np
data = np.array([8, 7, 5, 1, 2, 6, 5, 1, 2, 31, 9, 40, 9, 53, 4, 8, 3, 1, 46, 2, 18, 4, 17, 26, 17, 2, 19, 14, 2, 16, 35, 42, 22, 2, 19, 13, 59, 11, 69, 33, 2, 2, 24, 86, 16, 11, 7, 5, 18, 22, 1, 2, 16, 28, 3, 2, 12, 16, 1, 8, 1, 2, 5, 4, 9, 1, 1, 5, 1, 4, 5, 2, 11, 25, 6, 45, 64, 6, 2, 63, 26, 2, 3, 8, 3, 16, 8, 2, 2, 99, 2, 51, 43, 5, 53, 10, 19, 20, 6, 9, 1, 4, 1, 19, 4, 2, 3, 2, 77, 4, 7, 3, 2, 1, 81, 15, 50, 22, 58, 21, 10, 1, 18, 8, 1, 35, 2, 32, 18, 12, 11, 7, 5, 27, 29, 1, 2, 5, 1, 2, 3, 3, 1, 45, 22, 1, 12, 2, 21, 4, 1, 19, 27, 23, 3, 1, 21, 1, 124, 13, 17, 1, 18, 33, 23, 3, 6, 2, 8, 3, 1, 228, 28, 1, 1, 122, 868, 47, 2, 1, 9, 108, 10, 1, 5, 40, 43, 5, 2, 137, 9, 11, 19, 19, 11, 21, 8, 1, 6, 2, 3, 3, 26, 42, 14, 1, 14, 15, 3, 30, 17, 5, 17, 3, 38, 11, 54, 3, 1, 1, 3, 3, 7, 3, 1, 1, 5, 9, 1, 5, 4, 7, 35, 8, 10, 6, 6, 5, 3, 28, 2, 2, 5, 13, 6, 2, 4, 3, 2, 7, 52, 31, 1, 7, 7, 216, 4, 13, 6, 14, 4, 4, 5, 102, 3, 15, 4, 12, 48, 5, 9, 3, 10, 35, 36, 2, 10, 2, 55, 15, 17, 2, 19, 14, 14, 15, 5, 4, 11, 1, 1, 18, 4, 63, 63, 22, 37, 2, 22, 8, 22, 8, 20, 104, 3, 2, 6, 11, 20, 1, 3, 78, 2, 1, 52, 33, 2, 4, 9, 1, 27, 9, 4, 4, 2, 9, 9, 2, 24, 137, 12, 2, 2, 1, 6, 11, 8, 1, 20, 23, 75, 5, 1, 14, 3, 31, 15, 4, 2, 26, 50, 9, 75, 42, 14, 4, 1, 2, 9, 34, 25, 37, 53, 122, 28, 52, 22, 1, 109, 1, 1, 11, 1, 15, 2, 9, 32, 23, 5, 6, 3, 2, 51, 9, 12, 10, 7, 5, 2, 1, 311, 41, 1, 6, 13, 2, 5, 18, 105, 13, 17, 3, 9, 48, 2, 15, 18, 16, 77, 13, 3, 2, 2, 8, 1, 3, 4, 93, 23, 169, 1, 24, 2, 1, 8, 36, 1, 1, 1, 6, 3, 1, 25, 1, 2, 59, 2, 3, 3, 1, 8, 2, 1, 6, 15, 1, 7, 29, 4, 4, 8, 22, 5, 80, 16, 3, 147, 23, 6, 16, 1, 8, 530])
sns.set_style('ticks')
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))
sns.violinplot(y=np.log10(data), ax=ax1)
major_ticks = np.arange(np.floor(np.log10(data).min()), np.log10(data).max() + 1)
ax1.yaxis.set_ticks(major_ticks, minor=False)
ax1.yaxis.set_ticks([np.log10(x) for p in major_ticks for x in np.linspace(10 ** p, 10 ** (p + 1), 10)], minor=True)
ax1.yaxis.set_major_formatter(StrMethodFormatter("^{{{x:.0f}}}$"))
ax2.set_yscale('log')
sns.boxenplot(y=data, ax=ax2)
ymin, ymax = ax1.get_ylim()
ax2.set_ylim(10**ymin, 10**ymax)
plt.tight_layout()
plt.show()