数据不均匀的 Matplotlib 堆积图答案

【问题标题】：Matplotlib stacked plot with uneven data数据不均匀的 Matplotlib 堆积图
【发布时间】：2021-05-13 09:15:12
【问题描述】：

我想绘制一个堆叠直方图，但我有大小不均的数据列，而且确实是数据类型：

bovine_eta = bovine['Emissivity'].dropna()
equine_eta = equine['Emissivity'].dropna()
ovine_eta =  ovine['Emissivity'].dropna()

bovine_eta.sort()
equine_eta.sort()
ovine_eta.sort()


print(bovine_eta)
['0.93' '0.93' '0.93' '0.95' '0.95' '0.95' '0.95' '0.95' '0.95' '0.95' 
 '0.95' '0.95' '0.95' '0.95' '0.95' '0.95' '0.96' '0.96' '0.97' '0.97'
 '0.97' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98'
 '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98'
 '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98'
 '0.98' '0.985' '0.985' '0.985' '1']

print(equine_eta)
['0.95' '0.95' '0.95' '0.96' '0.97' '0.97' '0.97' '0.98' '0.98' '0.98', '0.98' '0.99' '0.99']

print(ovine_eta)
['0.95' '0.97' '0.97-0.98' '0.98' '0.98' '0.98' '0.98' '0.98' '0.98', '0.98' '0.98']

那我试试

plt.hist([bovine_eta, equine_eta, ovine_eta], stacked=True)

并得到结果图：
这看起来很奇怪，但显然离我真正想要的不远。你如何固定 x 轴？

【问题讨论】：

标签： python dataframe matplotlib histogram stacked

【解决方案1】：

主要问题是所有值都是字符串。对于直方图，它有助于将它们转换为浮点数。下一个问题是ovine 的第三个条目是一个不能转换为浮点数的字符串。 '0.97-0.98' 可以替换为中间值。

由于数据几乎是离散的，它有助于明确提供与这些值对齐的 bin 边缘。

import matplotlib.pyplot as plt
import numpy as np

bovine_eta = ['0.93','0.93','0.93','0.95','0.95','0.95','0.95','0.95','0.95','0.95','0.95','0.95','0.95','0.95','0.95','0.95','0.96','0.96','0.97','0.97','0.97','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.985','0.985','0.985','1']
equine_eta = ['0.95','0.95','0.95','0.96','0.97','0.97','0.97','0.98','0.98','0.98', '0.98','0.99','0.99']
ovine_eta = ['0.95','0.97','0.97-0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98','0.98']

ovine_eta[2] = '0.975'  # '0.97-0.98' isn't a valid number

bovine_eta = np.array(bovine_eta, dtype=float)
equine_eta = np.array(equine_eta, dtype=float)
ovine_eta = np.array(ovine_eta, dtype=float)

plt.hist([bovine_eta, equine_eta, ovine_eta],
         bins=np.arange(0.93, 1.01, 0.01),
         stacked=True, label=['bovine', 'equine', 'ovine'])
plt.legend()

【讨论】：