Seaborn条形图中条的升序 [英] Ascending order of bars in seaborn barplot

问题描述

我有以下数据框

   Class    Age Percentage
0   2004    3   43.491170
1   2004    2   29.616607
2   2004    4   13.838925
3   2004    6   10.049712
4   2004    5   2.637445
5   2004    1   0.366142
6   2005    2   51.267369
7   2005    3   19.589268
8   2005    6   13.730432
9   2005    4   11.155305
10  2005    5   3.343524
11  2005    1   0.913590
12  2005    9   0.000511

我想使用seaborn绘制条形图，其中y轴为百分比"，x轴为类别"，并使用年龄"列对其进行标记.我还想按降序排列条形，即从较大的条形到较小的条形.

I would like to make a bar plot using seaborn where in the y-axis is the 'Percentage', in the x-axis is the 'Class' and label them using the 'Age' column. I would also like to arrange the bars in descending order, i.e. from the bigger to the smaller bar.

为此，我想到了以下几点:我将根据百分比"变量的顺序更改 hue_order 参数.例如，如果我按 Class == 2004 的降序对百分比"列进行排序，则 hue_order = [3、2、4、6、5、1] .

In order to do that I thought of the following: I will change the hue_order parameter based on the order of the 'Percentage' variable. For example, if I sort the 'Percentage' column in descending order for the Class == 2004, then the hue_order = [3, 2, 4, 6, 5, 1].

这是我的代码:

import matplotlib.pyplot as plt
import seaborn as sns

def hue_order():
    for cls in dataset.Class.unique():
        temp_df = dataset[dataset['Class'] == cls]
        order = temp_df.sort_values('Percentage', ascending = False)['Age']  
    return order

sns.barplot(x="Class", y="Percentage", hue = 'Age', 
                 hue_order= hue_order(),  
                 data=dataset)
plt.show()

但是，仅对于 Class == 2005 ，条形按降序排列.有帮助吗?

However, the bars are in descending order only for the Class == 2005. Any help?

在我的问题中，我使用的是 hue 参数，因此，它不是建议的重复项.

In my question, I am using the hue parameter, thus, it is not a duplicate as proposed.

推荐答案

最原始的 hue 参数为绘图添加了另一个维度. hue_order 确定此维度的处理顺序.但是，您不能拆分该顺序.这意味着您可以更改顺序，以使 Age == 2 在图表中排在第三位.但是您不能对其进行部分更改，以使它在第一部分中处于其他位置，而在其他部分中则处于第三位置.

The seaborn hue parameter adds another dimension to the plot. The hue_order determines in which order this dimension is handled. However you cannot split that order. This means you may well change the order such that Age == 2 is in the third place in the plot. But you cannot change it partially, such that in some part it is in the first and in some other it'll be in the third place.

为了达到此处的期望，即在同一轴上使用不同顺序的辅助尺寸，您需要手动处理.

In order to achieve what is desired here, namely to use different orders of the auxilary dimensions within the same axes, you need to handle this manually.

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame({"Class" : [2004]*6+[2005]*7,
                   "Age" : [3,2,4,6,5,1,2,3,6,4,5,1,9],
                   "Percentage" : [50,40,30,20,10,30,20,35,40,50,45,30,15]})

def sortedgroupedbar(ax, x,y, groupby, data=None, width=0.8, **kwargs):
    order = np.zeros(len(data))
    df = data.copy()
    for xi in np.unique(df[x].values):
        group = data[df[x] == xi]
        a = group[y].values
        b = sorted(np.arange(len(a)),key=lambda x:a[x],reverse=True)
        c = sorted(np.arange(len(a)),key=lambda x:b[x])
        order[data[x] == xi] = c   
    df["order"] = order
    u, df["ind"] = np.unique(df[x].values, return_inverse=True)
    step = width/len(np.unique(df[groupby].values))
    for xi,grp in df.groupby(groupby):
        ax.bar(grp["ind"]-width/2.+grp["order"]*step+step/2.,
               grp[y],width=step, label=xi, **kwargs)
    ax.legend(title=groupby)
    ax.set_xticks(np.arange(len(u)))
    ax.set_xticklabels(u)
    ax.set_xlabel(x)
    ax.set_ylabel(y)


fig, ax = plt.subplots()    
sortedgroupedbar(ax, x="Class",y="Percentage", groupby="Age", data=df)
plt.show()

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