如何设置带有不同颜色误差条的标记？

如何解决如何设置带有不同颜色误差条的标记？

如何：

• 在图例中显示符号
• 颜色标记的方式与误差条相同（参数颜色给出错误：ValueError: RGBA 序列的长度应为 3 或 4
• 移除连接线 - 仅获取带有误差条的散点图

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D  # for legend handle

fig,ax = plt.subplots(figsize = (10,10))

times = [1,2,3,4,5]
rvs = [2,7]
sigma = [0.564,0.6,0.8,0.4]
rv_telescopes = ['A','B','A','C','C']

d = {'rv_times': times,'rv_rvs': rvs,'rv_sigma': sigma,'rv_telescopes': rv_telescopes }
df = pd.DataFrame(data=d)

colors = {'A':'#008f00','B':'#e36500','C':'red'}

plt.errorbar(df['rv_times'],df['rv_rvs'],df['rv_sigma'],marker = '_',ecolor = df['rv_telescopes'].map(colors),color = df['rv_telescopes'].map(colors),zorder = 1,ms = 30)


handles = [Line2D([0],[0],marker='_',color='w',markerfacecolor=v,label=k,markersize=10) for k,v in colors.items()]

ax.legend(handles=handles,loc='upper left',ncol = 2,fontsize=14)

plt.show()

编辑后

import matplotlib.pyplot as plt
from matplotlib.lines import Line2D  # for legend handle
import pandas as pd
import numpy as np

times = [1,'rv_telescopes': rv_telescopes}
df = pd.DataFrame(data=d)
colors = {'A': '#008f00','B': '#e36500','C': 'red'}

fig,ax = plt.subplots(figsize=(10,10))
ax.errorbar(df['rv_times'],color='none',ecolor=df['rv_telescopes'].map(colors),linewidth=1)
ax.scatter(df['rv_times'],linewidth=3,color=df['rv_telescopes'].map(colors),s=1000)

for rv_teles in np.unique(df['rv_telescopes']):
     color = colors[rv_teles]
     df1 = df[df['rv_telescopes'] == rv_teles]  # filter out rows corresponding to df['rv_telescopes']
     ax.errorbar(df1['rv_times'],df1['rv_rvs'],df1['rv_sigma'],color=color,ls='',ms=30,label=rv_teles)
ax.legend(loc='upper left',ncol=1,fontsize=14)
plt.show()

解决方法

plt.errorbar() 与带有额外参数的 plt.plot() 非常相似。因此，它主要使用单一颜色绘制折线图。可以通过 ecolor= 参数为误差线指定单独的颜色。但是，标记的颜色与折线图的颜色相同。可以通过空的 linestyle 来抑制折线图。最重要的是，plt.scatter() 可以绘制具有不同颜色的标记。

为了不将 'object-oriented' 与“函数式接口”混合，以下示例代码使用了 ax.errorbar() 和 ax.scatter()。

import matplotlib.pyplot as plt
from matplotlib.lines import Line2D  # for legend handle
import pandas as pd
import numpy as np

times = [1,2,3,4,5]
rvs = [2,7]
sigma = [0.564,0.6,0.8,0.4]
rv_telescopes = ['A','B','A','C','C']

d = {'rv_times': times,'rv_rvs': rvs,'rv_sigma': sigma,'rv_telescopes': rv_telescopes}
df = pd.DataFrame(data=d)
colors = {'A': '#008f00','B': '#e36500','C': 'red'}

fig,ax = plt.subplots(figsize=(10,10))
ax.errorbar(df['rv_times'],df['rv_rvs'],df['rv_sigma'],color='none',ecolor=df['rv_telescopes'].map(colors))
ax.scatter(df['rv_times'],marker='_',color=df['rv_telescopes'].map(colors),s=100)

handles = [Line2D([0],[0],linestyle='',color=v,label=k,markersize=10) for k,v in colors.items()]
ax.legend(handles=handles,loc='upper left',ncol=1,fontsize=14)
plt.show()

更简单的方法是多次调用 ax.errorbar()，每种颜色调用一次。这将自动创建适当的图例句柄：

for rv_teles in np.unique(df['rv_telescopes']):
     color = colors[rv_teles]
     df1 = df[df['rv_telescopes'] == rv_teles]  # filter out rows corresponding to df['rv_telescopes']
     ax.errorbar(df1['rv_times'],df1['rv_rvs'],df1['rv_sigma'],color=color,ls='',ms=30,label=rv_teles)
ax.legend(loc='upper left',fontsize=14)
plt.show()