python 画系统关联图_Python基于pyecharts实现关联图绘制
生活中有很多需要用到关联图的地方，至少我认为的是这样的图：https://www.echartsjs.com/examples/zh/editor.html?c=graph-npm
我是在使用Word2Vec计算关联词的余弦距离之后，想要更好的展示出来的时候，遇到的这种情况，就做了下拓展。
画图的步骤主要分为：
1. 将距离数据(或者相关数据)读入；
2. 按照一定的格式和参数将数据保存为json字符串；
3. 根据json串，绘制关联图。
具体而言，主要是：
<1>. 首先有一批数据，如图所示：
<2>. 导入所需要的包
import json
import pandas as pd
import random
import copy
<3>. 产生颜色随机值的函数
# 随机颜色
def randomcolor_func():
color_char = ["1","2","3","4","5","6","7","8","9","A","B","C","D","E","F"]
color_code = ""
for i in range(6):
color_code += color_char[random.randint(0,14)] # randint包括前后节点0和14
return "#"+color_code
<4>. 生成随机坐标
# 随机坐标
#生成随机数，浮点类型
def generate_position(n):
# n = 10
for i in range(n):
x = round(random.uniform(-2000, 2000), 5) #一定范围内的随机数，范围可变
y = round(random.uniform(-2000, 2000), 5) #控制随机数的精度round(数值，精度)
return x, y
<5>. 生成json格式的节点数据
def create_json(data, weights):
# 自定义节点
address_dict = {"nodes":[], "edges":[]}
node_dict = {
"color": "",
"label": "",
"attributes": {},
"y": None,
"x": None,
"id": "",
"size": None
}
edge_dict = {
"sourceID": "",
"attributes": {},
"targetID": "",
"size": None
}
# 给节点赋值
for ii in range(len(data)):
for jj in range(len(data.iloc[ii])):
# node，"attributes"属性可自行设置
node_dict[r"color"] = randomcolor_func()
node_dict[r"label"] = data.iloc[ii, jj]
x, y = generate_position(1)
node_dict[r"y"] = y
node_dict[r"x"] = x
node_dict[r"id"] = data.iloc[ii, jj]
node_dict[r"size"] = int(weights.loc[data.iloc[ii, jj]])
tmp_node = copy.deepcopy(node_dict)
address_dict[r"nodes"].append(tmp_node)
for ii in range(len(data)):
for jj in range(1, len(data.iloc[ii])):
# edge
edge_dict[r"sourceID"] = data.iloc[ii, 0]
edge_dict[r"targetID"] = data.iloc[ii, jj]
edge_dict[r"size"] = 2
tmp_edge = copy.deepcopy(edge_dict)
address_dict["edges"].append(tmp_edge)
return address_dict
<6>. 主函数生成json数据
if __name__ == "__main__":
# read data
data = pd.read_excel(r"test_josn_data.xlsx", 0)
weights = pd.DataFrame({"词频":[100, 40, 30, 20, 90, 50, 35, 14, 85, 38, 29, 10]},
index = ["球类","篮球","足球","羽毛球","美食","肯德基","火锅","烤鱼","饮料","可乐","红茶","奶茶"]) #建立索引权值列表
address_dict = create_json(data, weights)
with open("write_json.json", "w", encoding="utf-8") as f:
# json.dump(dict_, f) # 写为一行
json.dump(address_dict, f, indent=2, ensure_ascii=False) # 写为多行
最后形成的json数据如下：
<7>. 绘制关联图，里面的文件读取和保存地址自行修改，write_json.json 就是上面保存的json文件
import pyecharts.options as opts
from pyecharts.charts import Graph
import json
with open(r"D:Python_workspacespyder_spaceest_各种功能write_json.json", encoding="utf-8") as f: #设置以utf-8解码模式读取文件，encoding参数必须设置，否则默认以gbk模式读取文件，当文件中包含中文时，会报错
data = json.load(f)
#print(data)
nodes = [
{
"x": node["x"],
"y": node["y"],
"id": node["id"],
"name": node["label"],
"symbolSize": node["size"],
"itemStyle": {"normal": {"color": node["color"]}},
}
for node in data["nodes"]
]
edges = [{"source": edge["sourceID"], "target": edge["targetID"]} for edge in data["edges"]]
(
Graph(init_opts=opts.InitOpts(width="1600px", height="800px"))
.add(
series_name="",
nodes=nodes,
links=edges,
layout="none",
is_roam=True,
is_focusnode=True,
label_opts=opts.LabelOpts(is_show=True),
linestyle_opts=opts.LineStyleOpts(width=0.5, curve=0.3, opacity=0.7),
)
.set_global_opts(title_opts=opts.TitleOpts(title="热词对应的关联词"))
.render("关联词图.html")
)
最后，就生成了最开始的那张图。
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持云海天教程。