如何在直方图中添加范围？
How to add ranges in histogram?
我有一个名为 exceeded_amount 的值列，我想将其绘制为直方图。我这样做的方式，情节不清晰。
我如何将 bin 中的值分组到不同的范围内？例如 1-100、101-500、501-20000？
或者，请告知可视化 exceeded_amount 列的最佳方式。
tr['exceeded_amount'].plot()
根据 JohanC 更新
但是，我希望在轴上显示特定范围的数量，而不是 10^2、10^3...等
ax = sns.boxenplot(x=tr['exceeded_amount']) ax.set_xscale('log')
我想要什么？
[19.0,
193.0,
4928.0,
1956.0,
171.0,
163.7,
231.0,
5.0,
878.5,
190.46,
89.0,
4.0,
35.0,
393.0,
171.0,
546.0,
99.98,
93.36,
0.82,
419.14,
181.0,
42.27,
2807.0,
116.0,
1199.0,
16.0,
128.0,
412.0,
100.0,
1070.4,
461.0,
377.0,
266.0,
930.0,
625.99,
237.5,
157.67,
58.0,
870.88,
329.5,
1418.0,
391.0,
329.0,
182.81,
329.5,
98.0,
211.0,
1.0,
557.0,
1284.04,
131.0,
113.33,
64.0,
46.66,
598.48,
149.0,
561.0,
14.83,
209.0,
454.7,
273.33,
21.0,
724.0,
2226.0,
209.0,
23.0,
853.56,
89.0,
63.25,
28.0,
41.0,
303.5,
103.82,
162.01,
1763.0,
8.0,
2359.0,
1171.0,
194.68,
1031.0,
362.0,
333.0,
312.0,
854.65,
630.0,
833.0,
691.0,
227.0,
139.47,
277.56,
1642.0,
27.0,
166.0,
931.0,
968.7,
27.33,
338.0,
201.0,
77.0,
7547.04,
0.49,
568.0,
307.07,
203.0,
167.56,
1138.78,
111.0,
51.0,
423.0,
504.62,
353.97,
51.0,
416.0,
68.05,
16.0,
7.39,
631.0,
551.0,
596.0,
89.63,
777.0,
207.0,
167.56,
246.0,
503.99,
22.0,
65.79,
21.0,
747.0,
5058.26,
1673.0,
275.92,
108.66,
99.5,
893.0,
67.0,
49.0,
663.0,
72.6,
1824.66,
127.0,
239.71,
1306.0,
815.62,
100.88,
253.0,
636.0,
600.5,
321.0,
111.0,
545.3,
312.0,
17.0,
343.61,
5933.0,
310.0,
356.0,
284.0,
139.0,
877.0,
48.95,
715.0,
126.33,
1275.0,
149.0,
8.99,
71.0,
241.0,
116.0,
225.0,
882.07,
81.0,
121.0,
53.93,
496.0,
2636.85,
71.0,
81.0,
8222.0,
52.33,
114.0,
437.0,
95.0,
28967.0,
142.0,
1.0,
1271.2,
683.76,
184.0,
220.4,
182.0,
618.0,
119.67,
661.85,
71.0,
22.37,
570.4,
388.88,
113.0,
290.0,
137.03,
3879.0,
619.0,
720.45,
961.5,
11.0,
101.0,
14.0,
1189.0,
1038.0,
246.0,
422.0,
153.4,
6999.4,
288.4,
707.28,
22681.0,
698.0,
305.0,
1097.0,
91.0,
147.0,
4793.26,
26.0,
309.0,
37.66,
59.2,
422.0,
417.13,
344.99,
29.0,
437.0,
545.0,
695.0,
39.66,
380.0,
709.1,
291.0,
1596.0,
920753.0,
115.68,
145.19,
81.0,
764.0,
751.63,
766.93,
2141.0,
327.0,
1358.3,
381.0,
115.0,
116.0,
571.0,
84.0,
697.0,
33.0,
1589.0,
123.05,
11.5,
1297.0,
71.0,
427.99,
63.0,
153.99,
197.99,
168.99,
1271.2,
30.0,
671.0,
582.33,
445.08,
378.0,
114.5,
512.0,
739.5,
411.0,
58.0,
1263.0,
436.69,
26.53,
14467.99,
1.0,
1659.82,
50.0,
103.07,
364.0,
191.2,
761.0,
225.0,
645.0,
129.0,
185.0,
22.44,
292.06,
342.4,
3347.0,
76.0,
217.5,
870.99,
54.0,
1218.0,
210.51,
111.0,
252.0,
1597.4,
123.08,
556.0,
148.0,
131.0,
356.0,
178.12,
99341.0,
422.0,
163.0,
551.0,
1992.0,
176.0,
366.0,
263.0,
156.0,
213.0,
177.0,
1095.38,
83.0,
375.32,
750.0,
203.66,
554.0,
201.72,
225.0,
267.0,
637.95,
89.0,
76.0,
189.48,
1072.21,
13.0,
284.0,
86.0,
336.99,
33.53,
117.66,
100.99,
854.0,
2985.95,
157.99,
5.01,
322.0,
51.0,
408.0,
1331.0,
312.0,
281.0,
296.18,
287.0,
197.0,
557.08,
141.0,
556.0,
16.8,
1511.36,
27.35,
225.0,
841.0,
380.0,
1211.1,
1068.11,
529.31,
4372.0,
46.0,
181.0,
225.0,
135.0,
1655.66,
3865.0,
172.0,
286.0,
143.0,
1391.0,
65.0,
76.0,
1316.0,
2419.0,
893.0,
165.0,
196.0,
15.99,
537.27,
38.0,
51.0,
380.0,
265.0,
341.0,
276.38,
135.0,
716.0,
4915.0,
59.0,
130.0,
557.08,
3178.0,
1043.8,
473.0,
1938.99,
486.0,
2272.0,
61.0,
141.27,
312.0,
252.0,
79.0,
441.0,
21.0,
71.18,
44.0,
113.0,
2294.0,
1259.0,
120.08,
881.0,
280.39,
6.0,
18.0,
42.0,
209.0,
462.0,
152.0,
301.0,
244.0,
1110.0,
149.0,
877.0,
711.0,
1978.0,
184.95,
666.0,
322.0,
205.0,
309.0,
476.0,
3178.0,
1328.0,
428.0,
183.51,
63.0,
684.0,
254.42,
354.0,
116.0,
135.0,
144.67,
31.0,
136.0,
361.0,
272.09,
737.0,
3347.0,
363.74,
506.0,
209.99,
4827.0,
545.0,
412.0,
1636.0,
96.0,
238.0,
422.0,
109.0,
44.0,
287.0,
327.99,
349.19,
28.99,
279.0,
181.0,
629.0,
137.75,
71.0,
2357.0,
493.0,
340.0,
177.16,
71.2,
4819.74,
22.0,
71.0,
73.73,
343.0,
121.0,
2272.0,
201.56,
1831.0,
158.98,
493.0,
576.8,
260.97,
847.0,
73.0,
5.0,
251.0,
207.0,
174.0,
82.86,
131.0,
1053.0,
353.0,
101.0,
854.0,
259.77,
12.37,
385.0,
9.27,
286.0,
85.0,
98.14,
21.0,
31.0,
71.0,
178.0,
63.0,
517.38,
118.0,
2350.0,
143.0,
88.0,
61.0,
297.0,
64.15,
20.56,
117.0,
189.0,
177.0,
630.0,
2997.0,
9961.0,
236.0,
240.0,
459.99,
3.0,
608.0,
341.0,
11.0,
1052.0,
42.0,
341.0,
21.0,
395.0,
575.0,
635.99,
539.83,
30.0,
570.0,
75.0,
503.99,
3774.0,
446.0,
87.0,
113.66,
217.5,
489.0,
41.0,
626.99,
461.0,
514.88,
813.99,
43.62,
1663.0,
96.0,
276.06,
73.75,
302.0,
68.0,
651.0,
25.0,
34.0]
您可以明确设置自己的 bin 边缘，并将 x-axis 转换为对数刻度：
from matplotlib import pyplot as plt
from matplotlib.ticker import ScalarFormatter
import seaborn as sns
import numpy as np
values = [19.0, 193.0, 4928.0, 1956.0, 171.0, 163.7, 231.0, 5.0, 878.5, 190.46, 89.0, 4.0, 35.0, 393.0, 171.0, 546.0, 99.98, 93.36, 0.82, 419.14, 181.0, 42.27, 2807.0, 116.0, 1199.0, 16.0, 128.0, 412.0, 100.0, 1070.4, 461.0, 377.0, 266.0, 930.0, 625.99, 237.5, 157.67, 58.0, 870.88, 329.5, 1418.0, 391.0, 329.0, 182.81, 329.5, 98.0, 211.0, 1.0, 557.0, 1284.04, 131.0, 113.33, 64.0, 46.66, 598.48, 149.0, 561.0, 14.83, 209.0, 454.7, 273.33, 21.0, 724.0, 2226.0, 209.0, 23.0, 853.56, 89.0, 63.25, 28.0, 41.0, 303.5, 103.82, 162.01, 1763.0, 8.0, 2359.0, 1171.0, 194.68, 1031.0, 362.0, 333.0, 312.0, 854.65, 630.0, 833.0, 691.0, 227.0, 139.47, 277.56, 1642.0, 27.0, 166.0, 931.0, 968.7, 27.33, 338.0, 201.0, 77.0, 7547.04, 0.49, 568.0, 307.07, 203.0, 167.56, 1138.78, 111.0, 51.0, 423.0, 504.62, 353.97, 51.0, 416.0, 68.05, 16.0, 7.39, 631.0, 551.0, 596.0, 89.63, 777.0, 207.0, 167.56, 246.0, 503.99, 22.0, 65.79, 21.0, 747.0, 5058.26, 1673.0, 275.92, 108.66, 99.5, 893.0, 67.0, 49.0, 663.0, 72.6, 1824.66, 127.0, 239.71, 1306.0, 815.62, 100.88, 253.0, 636.0, 600.5, 321.0, 111.0, 545.3, 312.0, 17.0, 343.61, 5933.0, 310.0, 356.0, 284.0, 139.0, 877.0, 48.95, 715.0, 126.33, 1275.0, 149.0, 8.99, 71.0, 241.0, 116.0, 225.0, 882.07, 81.0, 121.0, 53.93, 496.0, 2636.85, 71.0, 81.0, 8222.0, 52.33, 114.0, 437.0, 95.0, 28967.0, 142.0, 1.0, 1271.2, 683.76, 184.0, 220.4, 182.0, 618.0, 119.67, 661.85, 71.0, 22.37, 570.4, 388.88, 113.0, 290.0, 137.03, 3879.0, 619.0, 720.45, 961.5, 11.0, 101.0, 14.0, 1189.0, 1038.0, 246.0, 422.0, 153.4, 6999.4, 288.4, 707.28, 22681.0, 698.0, 305.0, 1097.0, 91.0, 147.0, 4793.26, 26.0, 309.0, 37.66, 59.2, 422.0, 417.13, 344.99, 29.0, 437.0, 545.0, 695.0, 39.66, 380.0, 709.1, 291.0, 1596.0, 920753.0, 115.68, 145.19, 81.0, 764.0, 751.63, 766.93, 2141.0, 327.0, 1358.3, 381.0, 115.0, 116.0, 571.0, 84.0, 697.0, 33.0, 1589.0, 123.05, 11.5, 1297.0, 71.0, 427.99, 63.0, 153.99, 197.99, 168.99, 1271.2, 30.0, 671.0, 582.33, 445.08, 378.0, 114.5, 512.0, 739.5, 411.0, 58.0, 1263.0, 436.69, 26.53, 14467.99, 1.0, 1659.82, 50.0, 103.07, 364.0, 191.2, 761.0, 225.0, 645.0, 129.0, 185.0, 22.44, 292.06, 342.4, 3347.0, 76.0, 217.5, 870.99, 54.0, 1218.0, 210.51, 111.0, 252.0, 1597.4, 123.08, 556.0, 148.0, 131.0, 356.0, 178.12, 99341.0, 422.0, 163.0, 551.0, 1992.0, 176.0, 366.0, 263.0, 156.0, 213.0, 177.0, 1095.38, 83.0, 375.32, 750.0, 203.66, 554.0, 201.72, 225.0, 267.0, 637.95, 89.0, 76.0, 189.48, 1072.21, 13.0, 284.0, 86.0, 336.99, 33.53, 117.66, 100.99, 854.0, 2985.95, 157.99, 5.01, 322.0, 51.0, 408.0, 1331.0, 312.0, 281.0, 296.18, 287.0, 197.0, 557.08, 141.0, 556.0, 16.8, 1511.36, 27.35, 225.0, 841.0, 380.0, 1211.1, 1068.11, 529.31, 4372.0, 46.0, 181.0, 225.0, 135.0, 1655.66, 3865.0, 172.0, 286.0, 143.0, 1391.0, 65.0, 76.0, 1316.0, 2419.0, 893.0, 165.0, 196.0, 15.99, 537.27, 38.0, 51.0, 380.0, 265.0, 341.0, 276.38, 135.0, 716.0, 4915.0, 59.0, 130.0, 557.08, 3178.0, 1043.8, 473.0, 1938.99, 486.0, 2272.0, 61.0, 141.27, 312.0, 252.0, 79.0, 441.0, 21.0, 71.18, 44.0, 113.0, 2294.0, 1259.0, 120.08, 881.0, 280.39, 6.0, 18.0, 42.0, 209.0, 462.0, 152.0, 301.0, 244.0, 1110.0, 149.0, 877.0, 711.0, 1978.0, 184.95, 666.0, 322.0, 205.0, 309.0, 476.0, 3178.0, 1328.0, 428.0, 183.51, 63.0, 684.0, 254.42, 354.0, 116.0, 135.0, 144.67, 31.0, 136.0, 361.0, 272.09, 737.0, 3347.0, 363.74, 506.0, 209.99, 4827.0, 545.0, 412.0, 1636.0, 96.0, 238.0, 422.0, 109.0, 44.0, 287.0, 327.99, 349.19, 28.99, 279.0, 181.0, 629.0, 137.75, 71.0, 2357.0, 493.0, 340.0, 177.16, 71.2, 4819.74, 22.0, 71.0, 73.73, 343.0, 121.0, 2272.0, 201.56, 1831.0, 158.98, 493.0, 576.8, 260.97, 847.0, 73.0, 5.0, 251.0, 207.0, 174.0, 82.86, 131.0, 1053.0, 353.0, 101.0, 854.0, 259.77, 12.37, 385.0, 9.27, 286.0, 85.0, 98.14, 21.0, 31.0, 71.0, 178.0, 63.0, 517.38, 118.0, 2350.0, 143.0, 88.0, 61.0, 297.0, 64.15, 20.56, 117.0, 189.0, 177.0, 630.0, 2997.0, 9961.0, 236.0, 240.0, 459.99, 3.0, 608.0, 341.0, 11.0, 1052.0, 42.0, 341.0, 21.0, 395.0, 575.0, 635.99, 539.83, 30.0, 570.0, 75.0, 503.99, 3774.0, 446.0, 87.0, 113.66, 217.5, 489.0, 41.0, 626.99, 461.0, 514.88, 813.99, 43.62, 1663.0, 96.0, 276.06, 73.75, 302.0, 68.0, 651.0, 25.0, 34.0]
bins = 10.0 ** np.arange(-1, 7)
plt.figure(figsize=(12, 5))
ax = sns.histplot(x=values, bins=bins, edgecolor='k', linewidth=2)
ax.set_xscale('log')
ax.set_xticks(bins)
ax.xaxis.set_major_formatter(ScalarFormatter())
ax.ticklabel_format(axis='x', useOffset=False, style='plain')
plt.tight_layout()
plt.show()
这里是边的 10 次方乘以 1,2 或 5 的版本。
bins = np.outer(10.0 ** np.arange(-1, 7), [1, 2, 5]).ravel()[:-2]
plt.figure(figsize=(12, 5))
ax = sns.histplot(x=values, bins=bins, edgecolor='k', linewidth=2)
ax.set_xscale('log')
ax.set_xticks(bins)
ax.xaxis.set_major_formatter(lambda x, pos: f'{x:.1f}' if x < 1 else f'{x:.0f}' if x < 10000 else f'{x/1000:.0f}K')
ax.margins(x=0.01)
sns.despine()
plt.tight_layout()
plt.show()
相同的信息可以显示为条形图，使用 np.histogram 计算值：
bins = np.outer(10.0 ** np.arange(-1, 7), [1, 2, 5]).ravel()[:-2]
plt.figure(figsize=(16, 5))
heights, _ = np.histogram(values, bins=bins)
labels = [
f'{x0:.1f}-{x1:.1f}' if x0 < 1 else f'{x0:.0f}-{x1:.0f}' if x0 < 10000 else f'{x0 / 1000:.0f}-{x1 / 1000:.0f}K'
for x0, x1 in zip(bins[:-1], bins[1:])]
ax = sns.barplot(x=[lbl for lbl, h in zip(labels, heights) if h > 0], y=heights[heights > 0])
ax.margins(x=0.01)
sns.despine()
plt.tight_layout()
plt.show()
我有一个名为 exceeded_amount 的值列，我想将其绘制为直方图。我这样做的方式，情节不清晰。
我如何将 bin 中的值分组到不同的范围内？例如 1-100、101-500、501-20000？
或者，请告知可视化 exceeded_amount 列的最佳方式。
tr['exceeded_amount'].plot()
根据 JohanC 更新
但是，我希望在轴上显示特定范围的数量，而不是 10^2、10^3...等
ax = sns.boxenplot(x=tr['exceeded_amount']) ax.set_xscale('log')
我想要什么？
[19.0, 193.0, 4928.0, 1956.0, 171.0, 163.7, 231.0, 5.0, 878.5, 190.46, 89.0, 4.0, 35.0, 393.0, 171.0, 546.0, 99.98, 93.36, 0.82, 419.14, 181.0, 42.27, 2807.0, 116.0, 1199.0, 16.0, 128.0, 412.0, 100.0, 1070.4, 461.0, 377.0, 266.0, 930.0, 625.99, 237.5, 157.67, 58.0, 870.88, 329.5, 1418.0, 391.0, 329.0, 182.81, 329.5, 98.0, 211.0, 1.0, 557.0, 1284.04, 131.0, 113.33, 64.0, 46.66, 598.48, 149.0, 561.0, 14.83, 209.0, 454.7, 273.33, 21.0, 724.0, 2226.0, 209.0, 23.0, 853.56, 89.0, 63.25, 28.0, 41.0, 303.5, 103.82, 162.01, 1763.0, 8.0, 2359.0, 1171.0, 194.68, 1031.0, 362.0, 333.0, 312.0, 854.65, 630.0, 833.0, 691.0, 227.0, 139.47, 277.56, 1642.0, 27.0, 166.0, 931.0, 968.7, 27.33, 338.0, 201.0, 77.0, 7547.04, 0.49, 568.0, 307.07, 203.0, 167.56, 1138.78, 111.0, 51.0, 423.0, 504.62, 353.97, 51.0, 416.0, 68.05, 16.0, 7.39, 631.0, 551.0, 596.0, 89.63, 777.0, 207.0, 167.56, 246.0, 503.99, 22.0, 65.79, 21.0, 747.0, 5058.26, 1673.0, 275.92, 108.66, 99.5, 893.0, 67.0, 49.0, 663.0, 72.6, 1824.66, 127.0, 239.71, 1306.0, 815.62, 100.88, 253.0, 636.0, 600.5, 321.0, 111.0, 545.3, 312.0, 17.0, 343.61, 5933.0, 310.0, 356.0, 284.0, 139.0, 877.0, 48.95, 715.0, 126.33, 1275.0, 149.0, 8.99, 71.0, 241.0, 116.0, 225.0, 882.07, 81.0, 121.0, 53.93, 496.0, 2636.85, 71.0, 81.0, 8222.0, 52.33, 114.0, 437.0, 95.0, 28967.0, 142.0, 1.0, 1271.2, 683.76, 184.0, 220.4, 182.0, 618.0, 119.67, 661.85, 71.0, 22.37, 570.4, 388.88, 113.0, 290.0, 137.03, 3879.0, 619.0, 720.45, 961.5, 11.0, 101.0, 14.0, 1189.0, 1038.0, 246.0, 422.0, 153.4, 6999.4, 288.4, 707.28, 22681.0, 698.0, 305.0, 1097.0, 91.0, 147.0, 4793.26, 26.0, 309.0, 37.66, 59.2, 422.0, 417.13, 344.99, 29.0, 437.0, 545.0, 695.0, 39.66, 380.0, 709.1, 291.0, 1596.0, 920753.0, 115.68, 145.19, 81.0, 764.0, 751.63, 766.93, 2141.0, 327.0, 1358.3, 381.0, 115.0, 116.0, 571.0, 84.0, 697.0, 33.0, 1589.0, 123.05, 11.5, 1297.0, 71.0, 427.99, 63.0, 153.99, 197.99, 168.99, 1271.2, 30.0, 671.0, 582.33, 445.08, 378.0, 114.5, 512.0, 739.5, 411.0, 58.0, 1263.0, 436.69, 26.53, 14467.99, 1.0, 1659.82, 50.0, 103.07, 364.0, 191.2, 761.0, 225.0, 645.0, 129.0, 185.0, 22.44, 292.06, 342.4, 3347.0, 76.0, 217.5, 870.99, 54.0, 1218.0, 210.51, 111.0, 252.0, 1597.4, 123.08, 556.0, 148.0, 131.0, 356.0, 178.12, 99341.0, 422.0, 163.0, 551.0, 1992.0, 176.0, 366.0, 263.0, 156.0, 213.0, 177.0, 1095.38, 83.0, 375.32, 750.0, 203.66, 554.0, 201.72, 225.0, 267.0, 637.95, 89.0, 76.0, 189.48, 1072.21, 13.0, 284.0, 86.0, 336.99, 33.53, 117.66, 100.99, 854.0, 2985.95, 157.99, 5.01, 322.0, 51.0, 408.0, 1331.0, 312.0, 281.0, 296.18, 287.0, 197.0, 557.08, 141.0, 556.0, 16.8, 1511.36, 27.35, 225.0, 841.0, 380.0, 1211.1, 1068.11, 529.31, 4372.0, 46.0, 181.0, 225.0, 135.0, 1655.66, 3865.0, 172.0, 286.0, 143.0, 1391.0, 65.0, 76.0, 1316.0, 2419.0, 893.0, 165.0, 196.0, 15.99, 537.27, 38.0, 51.0, 380.0, 265.0, 341.0, 276.38, 135.0, 716.0, 4915.0, 59.0, 130.0, 557.08, 3178.0, 1043.8, 473.0, 1938.99, 486.0, 2272.0, 61.0, 141.27, 312.0, 252.0, 79.0, 441.0, 21.0, 71.18, 44.0, 113.0, 2294.0, 1259.0, 120.08, 881.0, 280.39, 6.0, 18.0, 42.0, 209.0, 462.0, 152.0, 301.0, 244.0, 1110.0, 149.0, 877.0, 711.0, 1978.0, 184.95, 666.0, 322.0, 205.0, 309.0, 476.0, 3178.0, 1328.0, 428.0, 183.51, 63.0, 684.0, 254.42, 354.0, 116.0, 135.0, 144.67, 31.0, 136.0, 361.0, 272.09, 737.0, 3347.0, 363.74, 506.0, 209.99, 4827.0, 545.0, 412.0, 1636.0, 96.0, 238.0, 422.0, 109.0, 44.0, 287.0, 327.99, 349.19, 28.99, 279.0, 181.0, 629.0, 137.75, 71.0, 2357.0, 493.0, 340.0, 177.16, 71.2, 4819.74, 22.0, 71.0, 73.73, 343.0, 121.0, 2272.0, 201.56, 1831.0, 158.98, 493.0, 576.8, 260.97, 847.0, 73.0, 5.0, 251.0, 207.0, 174.0, 82.86, 131.0, 1053.0, 353.0, 101.0, 854.0, 259.77, 12.37, 385.0, 9.27, 286.0, 85.0, 98.14, 21.0, 31.0, 71.0, 178.0, 63.0, 517.38, 118.0, 2350.0, 143.0, 88.0, 61.0, 297.0, 64.15, 20.56, 117.0, 189.0, 177.0, 630.0, 2997.0, 9961.0, 236.0, 240.0, 459.99, 3.0, 608.0, 341.0, 11.0, 1052.0, 42.0, 341.0, 21.0, 395.0, 575.0, 635.99, 539.83, 30.0, 570.0, 75.0, 503.99, 3774.0, 446.0, 87.0, 113.66, 217.5, 489.0, 41.0, 626.99, 461.0, 514.88, 813.99, 43.62, 1663.0, 96.0, 276.06, 73.75, 302.0, 68.0, 651.0, 25.0, 34.0]
您可以明确设置自己的 bin 边缘，并将 x-axis 转换为对数刻度：
from matplotlib import pyplot as plt
from matplotlib.ticker import ScalarFormatter
import seaborn as sns
import numpy as np
values = [19.0, 193.0, 4928.0, 1956.0, 171.0, 163.7, 231.0, 5.0, 878.5, 190.46, 89.0, 4.0, 35.0, 393.0, 171.0, 546.0, 99.98, 93.36, 0.82, 419.14, 181.0, 42.27, 2807.0, 116.0, 1199.0, 16.0, 128.0, 412.0, 100.0, 1070.4, 461.0, 377.0, 266.0, 930.0, 625.99, 237.5, 157.67, 58.0, 870.88, 329.5, 1418.0, 391.0, 329.0, 182.81, 329.5, 98.0, 211.0, 1.0, 557.0, 1284.04, 131.0, 113.33, 64.0, 46.66, 598.48, 149.0, 561.0, 14.83, 209.0, 454.7, 273.33, 21.0, 724.0, 2226.0, 209.0, 23.0, 853.56, 89.0, 63.25, 28.0, 41.0, 303.5, 103.82, 162.01, 1763.0, 8.0, 2359.0, 1171.0, 194.68, 1031.0, 362.0, 333.0, 312.0, 854.65, 630.0, 833.0, 691.0, 227.0, 139.47, 277.56, 1642.0, 27.0, 166.0, 931.0, 968.7, 27.33, 338.0, 201.0, 77.0, 7547.04, 0.49, 568.0, 307.07, 203.0, 167.56, 1138.78, 111.0, 51.0, 423.0, 504.62, 353.97, 51.0, 416.0, 68.05, 16.0, 7.39, 631.0, 551.0, 596.0, 89.63, 777.0, 207.0, 167.56, 246.0, 503.99, 22.0, 65.79, 21.0, 747.0, 5058.26, 1673.0, 275.92, 108.66, 99.5, 893.0, 67.0, 49.0, 663.0, 72.6, 1824.66, 127.0, 239.71, 1306.0, 815.62, 100.88, 253.0, 636.0, 600.5, 321.0, 111.0, 545.3, 312.0, 17.0, 343.61, 5933.0, 310.0, 356.0, 284.0, 139.0, 877.0, 48.95, 715.0, 126.33, 1275.0, 149.0, 8.99, 71.0, 241.0, 116.0, 225.0, 882.07, 81.0, 121.0, 53.93, 496.0, 2636.85, 71.0, 81.0, 8222.0, 52.33, 114.0, 437.0, 95.0, 28967.0, 142.0, 1.0, 1271.2, 683.76, 184.0, 220.4, 182.0, 618.0, 119.67, 661.85, 71.0, 22.37, 570.4, 388.88, 113.0, 290.0, 137.03, 3879.0, 619.0, 720.45, 961.5, 11.0, 101.0, 14.0, 1189.0, 1038.0, 246.0, 422.0, 153.4, 6999.4, 288.4, 707.28, 22681.0, 698.0, 305.0, 1097.0, 91.0, 147.0, 4793.26, 26.0, 309.0, 37.66, 59.2, 422.0, 417.13, 344.99, 29.0, 437.0, 545.0, 695.0, 39.66, 380.0, 709.1, 291.0, 1596.0, 920753.0, 115.68, 145.19, 81.0, 764.0, 751.63, 766.93, 2141.0, 327.0, 1358.3, 381.0, 115.0, 116.0, 571.0, 84.0, 697.0, 33.0, 1589.0, 123.05, 11.5, 1297.0, 71.0, 427.99, 63.0, 153.99, 197.99, 168.99, 1271.2, 30.0, 671.0, 582.33, 445.08, 378.0, 114.5, 512.0, 739.5, 411.0, 58.0, 1263.0, 436.69, 26.53, 14467.99, 1.0, 1659.82, 50.0, 103.07, 364.0, 191.2, 761.0, 225.0, 645.0, 129.0, 185.0, 22.44, 292.06, 342.4, 3347.0, 76.0, 217.5, 870.99, 54.0, 1218.0, 210.51, 111.0, 252.0, 1597.4, 123.08, 556.0, 148.0, 131.0, 356.0, 178.12, 99341.0, 422.0, 163.0, 551.0, 1992.0, 176.0, 366.0, 263.0, 156.0, 213.0, 177.0, 1095.38, 83.0, 375.32, 750.0, 203.66, 554.0, 201.72, 225.0, 267.0, 637.95, 89.0, 76.0, 189.48, 1072.21, 13.0, 284.0, 86.0, 336.99, 33.53, 117.66, 100.99, 854.0, 2985.95, 157.99, 5.01, 322.0, 51.0, 408.0, 1331.0, 312.0, 281.0, 296.18, 287.0, 197.0, 557.08, 141.0, 556.0, 16.8, 1511.36, 27.35, 225.0, 841.0, 380.0, 1211.1, 1068.11, 529.31, 4372.0, 46.0, 181.0, 225.0, 135.0, 1655.66, 3865.0, 172.0, 286.0, 143.0, 1391.0, 65.0, 76.0, 1316.0, 2419.0, 893.0, 165.0, 196.0, 15.99, 537.27, 38.0, 51.0, 380.0, 265.0, 341.0, 276.38, 135.0, 716.0, 4915.0, 59.0, 130.0, 557.08, 3178.0, 1043.8, 473.0, 1938.99, 486.0, 2272.0, 61.0, 141.27, 312.0, 252.0, 79.0, 441.0, 21.0, 71.18, 44.0, 113.0, 2294.0, 1259.0, 120.08, 881.0, 280.39, 6.0, 18.0, 42.0, 209.0, 462.0, 152.0, 301.0, 244.0, 1110.0, 149.0, 877.0, 711.0, 1978.0, 184.95, 666.0, 322.0, 205.0, 309.0, 476.0, 3178.0, 1328.0, 428.0, 183.51, 63.0, 684.0, 254.42, 354.0, 116.0, 135.0, 144.67, 31.0, 136.0, 361.0, 272.09, 737.0, 3347.0, 363.74, 506.0, 209.99, 4827.0, 545.0, 412.0, 1636.0, 96.0, 238.0, 422.0, 109.0, 44.0, 287.0, 327.99, 349.19, 28.99, 279.0, 181.0, 629.0, 137.75, 71.0, 2357.0, 493.0, 340.0, 177.16, 71.2, 4819.74, 22.0, 71.0, 73.73, 343.0, 121.0, 2272.0, 201.56, 1831.0, 158.98, 493.0, 576.8, 260.97, 847.0, 73.0, 5.0, 251.0, 207.0, 174.0, 82.86, 131.0, 1053.0, 353.0, 101.0, 854.0, 259.77, 12.37, 385.0, 9.27, 286.0, 85.0, 98.14, 21.0, 31.0, 71.0, 178.0, 63.0, 517.38, 118.0, 2350.0, 143.0, 88.0, 61.0, 297.0, 64.15, 20.56, 117.0, 189.0, 177.0, 630.0, 2997.0, 9961.0, 236.0, 240.0, 459.99, 3.0, 608.0, 341.0, 11.0, 1052.0, 42.0, 341.0, 21.0, 395.0, 575.0, 635.99, 539.83, 30.0, 570.0, 75.0, 503.99, 3774.0, 446.0, 87.0, 113.66, 217.5, 489.0, 41.0, 626.99, 461.0, 514.88, 813.99, 43.62, 1663.0, 96.0, 276.06, 73.75, 302.0, 68.0, 651.0, 25.0, 34.0]
bins = 10.0 ** np.arange(-1, 7)
plt.figure(figsize=(12, 5))
ax = sns.histplot(x=values, bins=bins, edgecolor='k', linewidth=2)
ax.set_xscale('log')
ax.set_xticks(bins)
ax.xaxis.set_major_formatter(ScalarFormatter())
ax.ticklabel_format(axis='x', useOffset=False, style='plain')
plt.tight_layout()
plt.show()
这里是边的 10 次方乘以 1,2 或 5 的版本。
bins = np.outer(10.0 ** np.arange(-1, 7), [1, 2, 5]).ravel()[:-2]
plt.figure(figsize=(12, 5))
ax = sns.histplot(x=values, bins=bins, edgecolor='k', linewidth=2)
ax.set_xscale('log')
ax.set_xticks(bins)
ax.xaxis.set_major_formatter(lambda x, pos: f'{x:.1f}' if x < 1 else f'{x:.0f}' if x < 10000 else f'{x/1000:.0f}K')
ax.margins(x=0.01)
sns.despine()
plt.tight_layout()
plt.show()
相同的信息可以显示为条形图，使用 np.histogram 计算值：
bins = np.outer(10.0 ** np.arange(-1, 7), [1, 2, 5]).ravel()[:-2]
plt.figure(figsize=(16, 5))
heights, _ = np.histogram(values, bins=bins)
labels = [
f'{x0:.1f}-{x1:.1f}' if x0 < 1 else f'{x0:.0f}-{x1:.0f}' if x0 < 10000 else f'{x0 / 1000:.0f}-{x1 / 1000:.0f}K'
for x0, x1 in zip(bins[:-1], bins[1:])]
ax = sns.barplot(x=[lbl for lbl, h in zip(labels, heights) if h > 0], y=heights[heights > 0])
ax.margins(x=0.01)
sns.despine()
plt.tight_layout()
plt.show()