losing_mae.tail(10)
238 -500.0
239 -637.5
240 -412.5
242 -1062.5
243 -562.5
245 -412.5
247 -437.5
252 -800.0
254 -662.5
255 -1062.5
Name: mae, Length: 113, dtype: float64
losing_mae.hist(ax=ax, bins=25, color='c', alpha=0.5)
losing_mae.plot.kde(color='c', ax=ax2, lw=1)
答案 0 :(得分:1)
设置:
Child
import numpy as np
import pandas as pd
losing_mae = pd.DataFrame.from_dict({1: {0: -500.0,
1: -637.5,
2: -412.5,
3: -1062.5,
4: -562.5,
5: -412.5,
6: -437.5,
7: -800.0,
8: -662.5,
9: -1062.5}}
图返回一个axes
对象。您可以向下查找x和y:
kde
给出对象列表。您可能想深入研究d = losing_mae.plot.kde()
print(d.get_children())
:
Line2D
现在抓住该行及其[<matplotlib.lines.Line2D at 0x7fb82ce67550>,
<matplotlib.spines.Spine at 0x7fb82d237e80>,
<matplotlib.spines.Spine at 0x7fb84003cd30>,
<matplotlib.spines.Spine at 0x7fb82d221b38>,
<matplotlib.spines.Spine at 0x7fb82d221748>,
<matplotlib.axis.XAxis at 0x7fb82d2590f0>,
<matplotlib.axis.YAxis at 0x7fb82d221400>,
Text(0.5, 1.0, ''),
Text(0.0, 1.0, ''),
Text(1.0, 1.0, ''),
<matplotlib.legend.Legend at 0x7fb82ce67400>,
<matplotlib.patches.Rectangle at 0x7fb82cea6940>]
,然后您可以得到path
:
vertices
分隔X和Y:
l = d.get_children()[0].get_path()
l = l.vertices
print(l)
array([[-1.38750000e+03, 5.87608940e-05],
[-1.38619870e+03, 5.97906082e-05],
[-1.38489740e+03, 6.08341884e-05],
.... # and so on for ~2000 points
然后您可以同时致电x, y = np.split(l.vertices, 2, 1)
以获得所需的积分:
max