第 25 行创建了一个图表,其中 node 和 link是字典。node 使用的 label 对象由唯一的 Depts 院系组成,而 link 使用的两个列表分别由 sending"院系的索引和 acepting 院系的索引组成。 importpandasaspd importplotly.graph_objectsasgr data={ 'Sending_Dept':['5A','4A','5B','5A','4B','4A','3A',...
dept_index=Dept_indices[dept]accepting_indices.append(dept_index)flowvalues=df['FlowValue'].tolist()# Sankey diagramfig=gr.Figure(data=[gr.Sankey(node=dict(pad=10,thickness=25,line=dict(color="red",width=0.8),label=Depts,),link=dict(source=sending_indices,target=accepting_indices,value=flow...
AI代码解释 NODES=dict(#012345label=["United States of America","People's Republic of China","Japan","Gold","Silver","Bronze"],color=["seagreen","dodgerblue","orange","gold","silver","brown"],)x=[0,0,0,0.5,0.5,0.5],y=[0,0.5,1,0.1,0.5,1],)data=go.Sankey(node=NODES,link=...
import urllib from bs4 import BeautifulSoup url = "Example Domain" html = urllib.urlopen(url).read() soup = BeautifulSoup(html, "html.parser") # 提取网页中的文本 text = soup.get_text() # 提取网页中的链接 links = [] for link in soup.find 然后你知道要学爬虫的话,得先学习Python的基础...
Sankey( valueformat=".0f", valuesuffix="TWh", # 点 node=dict( pad=15, thickness=15, line=dict(color = "black", width = 0.5), label=data['data'][0]['node']['label'], color=data['data'][0]['node']['color'] ), # 线 link=dict( source=data['data'][0]['link']['...
['node']['label'], color=data['data'][0]['node']['color'] ), # 线 link=dict( source=data['data'][0]['link']['source'], target=data['data'][0]['link']['target'], value=data['data'][0]['link']['value'], label=data['data'][0]['link']['label'], color=data[...
(r"$Node ID$")ax.set_ylabel(r"$Disp/m$")ax.set_ylim([-4e-2,4e-2])ax.set_xlim([-1,27])ax.xaxis.set_minor_locator(MultipleLocator(1))ax.plot(range(len(disp)),disp,"r*-")ax2.set_xlabel(r"$Element ID$")ax2.set_xlim([-1,46])ax2...
edge_color='source', node_color='index', width=700, height=700 ) ).select(value=(5, None)) # Display the plot chord 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. ...
NODE: 所有桑基图的节点集合 link: 每一个数据流的起始,结束,与值。 核心统计原理是: 先确定从左到右的统计大纲:在本次案例中为: 接下来就是找大纲序列中当前大类中对应下一级的小类的数目,例: 案例第一步即找最高车速段中 最高车速能力在<180km/h的筛选出来,同时找出低速度段且续驶里程>80km的数量...
node = dict(label = label, pad=200, thickness=20) # 节点数据,间隔和厚度设置 # 添加绘图数据 data = go.Sankey(link = link, node=node) # 绘图并显示 fig = go.Figure(data) fig.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. ...