Django 404尾随斜线(Django 404 on trailing slash)

 您可以使用sns.boxplot和sns.stripplot函数中的order参数来订购“框”。 下面是一个如何执行此操作的示例（因为“最大到最低”并不完全清楚您的意思，即您希望根据条目对条目进行排序，我假设您要根据总和对它们进行排序对于他们的“距离”值，如果要根据不同的值对它们进行排序，您应该能够编辑解决方案以满足您的需求）： 
 
import numpy as np
import seaborn as sns
sns.set(style="ticks", palette="muted", color_codes=True)

# Load the example planets dataset
planets = sns.load_dataset("planets")

# Determine the order of boxes
order = planets.groupby(by=["method"])["distance"].sum().iloc[::-1].index

# Plot the orbital period with horizontal boxes
ax = sns.boxplot(x="distance", y="method", data=planets,
                 order=order, whis=np.inf, color="c")

# Add in points to show each observation
sns.stripplot(x="distance", y="method", data=planets, order=order,
              jitter=True, size=3, color=".3", linewidth=0)


# Make the quantitative axis logarithmic
ax.set_xscale("log")
sns.despine(trim=True)
 
 这个修改过的代码（注意在sns.boxplot和sns.stripplot函数中使用order参数）将产生下图： 
 
  

You can use the order argument in the sns.boxplot and sns.stripplot functions to order your "boxes". Here is an example of how to do this (since it is not completely clear what you mean by "greatest to lowest", i.e. which variable you want to sort the entries based on, I am assume you want to sort them based on the sum of their "distance" values, you should be able to edit the solution to fit your needs if you want to sort them based on a different value):
 
import numpy as np
import seaborn as sns
sns.set(style="ticks", palette="muted", color_codes=True)

# Load the example planets dataset
planets = sns.load_dataset("planets")

# Determine the order of boxes
order = planets.groupby(by=["method"])["distance"].sum().iloc[::-1].index

# Plot the orbital period with horizontal boxes
ax = sns.boxplot(x="distance", y="method", data=planets,
                 order=order, whis=np.inf, color="c")

# Add in points to show each observation
sns.stripplot(x="distance", y="method", data=planets, order=order,
              jitter=True, size=3, color=".3", linewidth=0)


# Make the quantitative axis logarithmic
ax.set_xscale("log")
sns.despine(trim=True)
 
This modifed code (notice the use of the order argument in the sns.boxplot and sns.stripplot functions) will produce the following figure: