Python中具有图例和范围的并行图

如何解决Python中具有图例和范围的并行图

我正在寻找一个生成平行图的代码，例如：[在此处输入图像描述] [1]

但是我不明白。我正在使用冲积库，但正在获取此图表：[在此处输入图片描述] [2] 但是我要考虑一个值范围来放置图例，并且考虑到值在确定的范围内，线的宽度必须具有固定的宽度。 我的数据库是：

[在此处输入图片描述] [3]

我的完整代码是：

import alluvial
#import sankey
import pySankey
from pySankey import sankey
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
pd.options.display.max_rows=8
import csv
from operator import itemgetter
import networkx as nx
from networkx.algorithms import community #This part of networkx,for community detection,import os
#import alluvial
import matplotlib.pyplot as plt
import matplotlib.cm
import numpy as np
import pandas as pd
os.chdir('C:\\Users\\CESAR.LAPTOP-1PMB3UGT\\Desktop\\Payments,Currencies and Infrastructure Division\\Tanai project')


database = pd.read_excel('network_analisis.xlsx',sheet_name = 'Data')
classification = pd.read_excel('Classifcation.xlsx')
a = classification['Country'].unique().tolist() 
b =database['Countries'].unique().tolist()  
matches = [x for x in a if x in b]
not_matches = [x for x in a if x not in b]
classification1 = classification
for a in classification1['Country']:
    for b in not_matches:
        if a == b:
           classification1 = classification1.drop(classification1[classification1.Country == a].index)
database_1 = database.merge(classification1,left_on='Countries',right_on='Country')
del database_1['RES']
del database_1['TOT']
database_1['Range'] = 0 
for i in range(len(database_1['Countries'])):
     if database_1['USD'][i] <= 10000:
            database_1['Range'][i] = '[0,10000]'
     elif database_1['USD'][i] > 10000 and database_1['USD'][i] <= 100000 :
            database_1['Range'][i] = '[10001,100000]'  
     elif database_1['USD'][i] > 100001 and database_1['USD'][i] <= 500000 :
            database_1['Range'][i] = '[100001,500000]'    
            


datanuueva = database_1.melt(id_vars=["Countries",'Country','Code','ISO','Region (IMF)','Region (WB)','IMF Regional Technical Assistance Center (R-TAC)','Exchange Arrangement Classification','Market Type (IMF)','Income Level (World Bank) 2018','Monetary Union','Range'],var_name="Currencies",value_name="Values")
datanuueva.sort_values(by=['Countries'],inplace =True)
datanuueva1 = datanuueva[datanuueva['Countries'] != 'Total' ]
datanuueva2 = datanuueva1[datanuueva1['Values'] != '..' ]
datanuueva3 =  datanuueva2[datanuueva2['Values'] != 0 ]
datanuueva4 =  datanuueva3.dropna()
APD = datanuueva3[datanuueva3['Region (IMF)'] == 'Asia & Pacific']
APD1 = APD.reset_index(drop=True) 
df = APD1[['Countries','Currencies']]
 
#APD1 = APD.reset_index(drop=True) 
input_data = df.values.tolist()
 
# Plotting:
 
cmap = matplotlib.cm.get_cmap('jet')
ax = alluvial.plot(
    input_data,alpha=0.4,color_side=1,rand_seed=seed,disp_width=True,wdisp_sep=' '*4,cmap=cmap,fontname='Monospace',labels=('Countries','Currencies'),label_shift=2,linewidth=20)
ax.set_title('OTC Ex Turnover by Country and Currency in April 2019',fontsize=20,fontname='Monospace')
plt.savefig('alluvial_graph.png')
plt.show()

任何建议将不胜感激。 [1]：https://i.stack.imgur.com/us0QE.png [2]：https://i.stack.imgur.com/CUYrV.png [3]：https://i.stack.imgur.com/SQCO8.png