使用 Seaborn 绘制具有最小/最大着色的时间序列图

I am trying to create a 3-line time series plot based on the following data , in a Week x Overload graph, where each Cluster is a different line.

我对每个（集群、周）对都有多个观察结果（每个 atm 5 个，将有 1000 个）。我希望线上的点是该特定（集群、周）对的平均过载值，而范围是它的最小/最大值。

目前使用以下代码来绘制它，但我没有得到任何行，因为我不知道使用当前数据帧指定什么单位：

    ax14 = sns.tsplot(data = long_total_cluster_capacity_overload_df, value = "Overload", time = "Week", condition = "Cluster")

胃肠道间质瘤数据

I have a feeling I still need to re-shape my dataframe, but I have no idea how. Looking for a final results that looks like this

基于这个令人难以置信的答案，我能够创建一个猴子补丁来完美地完成您正在寻找的事情。

import pandas as pd
import seaborn as sns    
import seaborn.timeseries

def _plot_range_band(*args, central_data=None, ci=None, data=None, **kwargs):
    upper = data.max(axis=0)
    lower = data.min(axis=0)
    #import pdb; pdb.set_trace()
    ci = np.asarray((lower, upper))
    kwargs.update({"central_data": central_data, "ci": ci, "data": data})
    seaborn.timeseries._plot_ci_band(*args, **kwargs)

seaborn.timeseries._plot_range_band = _plot_range_band

cluster_overload = pd.read_csv("TSplot.csv", delim_whitespace=True)
cluster_overload['Unit'] = cluster_overload.groupby(['Cluster','Week']).cumcount()

ax = sns.tsplot(time='Week',value="Overload", condition="Cluster", unit="Unit", data=cluster_overload,
               err_style="range_band", n_boot=0)

Output Graph:

请注意，阴影区域与折线图中的真实最大值和最小值对齐！

如果你明白为什么unit需要变量，请告诉我。

如果您不希望它们全部出现在同一个图表上，那么：

import pandas as pd
import seaborn as sns
import seaborn.timeseries


def _plot_range_band(*args, central_data=None, ci=None, data=None, **kwargs):
    upper = data.max(axis=0)
    lower = data.min(axis=0)
    #import pdb; pdb.set_trace()
    ci = np.asarray((lower, upper))
    kwargs.update({"central_data": central_data, "ci": ci, "data": data})
    seaborn.timeseries._plot_ci_band(*args, **kwargs)

seaborn.timeseries._plot_range_band = _plot_range_band

cluster_overload = pd.read_csv("TSplot.csv", delim_whitespace=True)
cluster_overload['subindex'] = cluster_overload.groupby(['Cluster','Week']).cumcount()

def customPlot(*args,**kwargs):
    df = kwargs.pop('data')
    pivoted = df.pivot(index='subindex', columns='Week', values='Overload')
    ax = sns.tsplot(pivoted.values, err_style="range_band", n_boot=0, color=kwargs['color'])

g = sns.FacetGrid(cluster_overload, row="Cluster", sharey=False, hue='Cluster', aspect=3)
g = g.map_dataframe(customPlot, 'Week', 'Overload','subindex')

Which produces the following, (you can obviously play with the aspect ratio if you think the proportions are off)