来自分位数的 python 扇形图

Question

我想在类似的图中可视化我的数据，以便数据间隔运行从第 50 个百分位数的最暗阴影到第 10 个百分位数的最亮阴影底部和顶部间隔的第 90 个。

我已经计算了我的时间序列的分位数，并将它们放在数据框中

我想要看起来像这张图片的东西。

我可以制作这样的图表，但不一样

我的数据框看起来像这样

Time | pct0.1 | pct0.2 | pct0.3 | pct0.4 | pct0.5 | pct0.6 | pct0.7 | pct0.8 | pct0.9
01:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
02:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
03:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
04:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
05:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
06:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
07:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
08:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4 | 1.2
09:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 46.2 | 138.6
10:00 | 246.4 | 340.8 | 445.0 | 559.0 | 673.0 | 737.8 | 802.6 | 843.2 | 859.6
11:00 | 429.8 | 620.6 | 777.8 | 901.4 | 1025.0 | 1153.8 | 1282.6 | 1362.8 | 1394.4
12:00 | 559.2 | 840.4 | 1025.8 | 1115.4 | 1205.0 | 1367.8 | 1530.6 | 1630.4 | 1667.2
13:00 | 606.4 | 968.8 | 1154.8 | 1164.4 | 1174.0 | 1351.2 | 1528.4 | 1648.0 | 1710.0
14:00 | 514.4 | 856.8 | 1031.8 | 1039.4 | 1047.0 | 1232.2 | 1417.4 | 1541.2 | 1603.6
15:00 | 386.0 | 620.0 | 760.4 | 807.2 | 854.0 | 1026.8 | 1199.6 | 1309.0 | 1355.0
16:00 | 259.0 | 331.0 | 391.4 | 440.2 | 489.0 | 621.4 | 753.8 | 836.6 | 869.8
17:00 | 87.2 | 100.4 | 110.2 | 116.6 | 123.0 | 174.2 | 225.4 | 252.6 | 255.8
18:00 | 0.4 | 0.8 | 1.6 | 2.8 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0
19:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
20:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
21:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
22:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
23:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
00:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0

在此先感谢您的帮助

Answer 1

您可以使用 ax.fill_between() 为分位数之间的范围着色：

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from io import StringIO

data_str = '''
Time | pct0.1 | pct0.2 | pct0.3 | pct0.4 | pct0.5 | pct0.6 | pct0.7 | pct0.8 | pct0.9
01:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
02:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
03:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
04:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
05:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
06:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
07:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
08:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4 | 1.2
09:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 46.2 | 138.6
10:00 | 246.4 | 340.8 | 445.0 | 559.0 | 673.0 | 737.8 | 802.6 | 843.2 | 859.6
11:00 | 429.8 | 620.6 | 777.8 | 901.4 | 1025.0 | 1153.8 | 1282.6 | 1362.8 | 1394.4
12:00 | 559.2 | 840.4 | 1025.8 | 1115.4 | 1205.0 | 1367.8 | 1530.6 | 1630.4 | 1667.2
13:00 | 606.4 | 968.8 | 1154.8 | 1164.4 | 1174.0 | 1351.2 | 1528.4 | 1648.0 | 1710.0
14:00 | 514.4 | 856.8 | 1031.8 | 1039.4 | 1047.0 | 1232.2 | 1417.4 | 1541.2 | 1603.6
15:00 | 386.0 | 620.0 | 760.4 | 807.2 | 854.0 | 1026.8 | 1199.6 | 1309.0 | 1355.0
16:00 | 259.0 | 331.0 | 391.4 | 440.2 | 489.0 | 621.4 | 753.8 | 836.6 | 869.8
17:00 | 87.2 | 100.4 | 110.2 | 116.6 | 123.0 | 174.2 | 225.4 | 252.6 | 255.8
18:00 | 0.4 | 0.8 | 1.6 | 2.8 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0
19:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
20:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
21:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
22:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
23:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
00:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0'''

df = pd.read_csv(StringIO(data_str), sep='\s+\|\s+', engine='python')

fig, ax = plt.subplots(figsize=(12, 4))
xs = np.arange(len(df))
colors = plt.cm.Reds(np.linspace(0.3, 0.8, 4))
for lower, upper, color in zip([f'pct0.{i}' for i in range(1, 5)], [f'pct0.{i}' for i in range(9, 5, -1)], colors):
    ax.fill_between(xs, df[lower], df[upper], color=color, label=lower + '-' + upper)
ax.plot(xs, df['pct0.5'], color='black', lw=2, label='Median')
ax.set_xticks(xs)
ax.set_xticklabels(df['Time'])
ax.legend()
ax.margins(x=0)
ax.set_ylim(ymin=0)
for sp in ['top', 'right']:
    ax.spines[sp].set_visible(False)
plt.tight_layout()
plt.show()

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