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一、JSON

1.JSON介绍

JSON是一种轻量级的数据交互格式，采用完全独立于编程语言的文本格式来存储和表示数据（就是字符串）。
Python语言使用JSON有很大优势，因为JSON无非就是一个单独的字典或一个内部元素都是字典的列表。
总结所以JSON可以直接和Python的字典或列表进行无缝转换。

2.JSON格式数据转化

通过 json.dumps(data) 方法把python数据转化为了json数据

data = json.dumps(data)

如果有中文可以带上:ensure ascii=False参数来确保中文正常转换
通过 json.loads(data) 方法把json数据转化为了 python列表或字典

data = json.loads(data)

3.示例

echarts/__init__.pydata.txtjsonData.py

data.txt

{"code": 10000,"msg": null,"error": true,"data": {"total": 1664,"items": [{"stat_date": "2024-01-09","genome": 1,"industrial": 3,"literature": 3,"patent": 6},{"stat_date": "2024-01-08","genome": 3,"industrial": 8,"literature": 6,"patent": 9},{"stat_date": "2024-01-07","genome": 3,"industrial": 5,"literature": 7,"patent": 6},{"stat_date": "2024-01-06","genome": 5,"industrial": 7,"literature": 3,"patent": 8},{"stat_date": "2024-01-05","genome": 9,"industrial": 7,"literature": 5,"patent": 7},{"stat_date": "2024-01-04","genome": 3,"industrial": 5,"literature": 8,"patent": 5},{"stat_date": "2024-01-03","genome": 8,"industrial": 0,"literature": 8,"patent": 6},{"stat_date": "2024-01-02","genome": 0,"industrial": 9,"literature": 4,"patent": 4},{"stat_date": "2024-01-01","genome": 7,"industrial": 8,"literature": 0,"patent": 3},{"stat_date": "2024-01-10","genome": 3,"industrial": 7,"literature": 4,"patent": 6}],"has_more": true}
}

jsonData.py

import jsondef formatData():# 日期列表date_data = {}date = []# 数据genome_data = []industrial_data = []literature_data = []patent_data = []try:with open("D:/test/demo/echarts/data.txt","r",encoding="utf-8") as file:for line in file:line = line.strip()if len(line.split(":")) == 1:continuedata = line.split(":")[1].replace('"',"").strip(" ,")if line.startswith('"stat_date"'):date.append(data)elif line.startswith('"genome"'):genome_data.append(data)elif line.startswith('"industrial"'):industrial_data.append(data)elif line.startswith('"literature"'):literature_data.append(data)elif line.startswith('"patent"'):patent_data.append(data)except Exception as e:print(f"出现异常啦：{e}")date_data["date"] = datedate_json = json.dumps(date_data)genome_json = json.dumps(genome_data)industrial_json = json.dumps(industrial_data)literature_json = json.dumps(literature_data)patent_json = json.dumps(patent_data)print(f"{type(date_json)},{date_json}")print(f"{type(genome_json)},{genome_json}")print(f"{type(industrial_json)},{industrial_json}")print(f"{type(literature_json)},{literature_json}")print(f"{type(patent_json)},{patent_json}")return date_json,genome_json,industrial_json,literature_json,patent_json

输出：

<class 'str'>,{"date": ["2024-01-09", "2024-01-09", "2024-01-09", "2024-01-09", "2024-01-09", "2024-01-09", "2024-01-09", "2024-01-09", "2024-01-09", "2024-01-09"]}
<class 'str'>,["1", "3", "3", "5", "9", "3", "8", "0", "7", "3"]
<class 'str'>,["3", "8", "5", "7", "7", "5", "0", "9", "8", "7"]
<class 'str'>,["3", "6", "7", "3", "5", "8", "8", "4", "0", "4"]
<class 'str'>,["6", "9", "6", "8", "7", "5", "6", "4", "3", "6"]

二、pyecharts

开发可视化图表使用的技术栈是Echarts框架的python版本：pyecharts包。

1.安装pyecharts包

通过pip下载pyecharts包，在开发过程中直接引用即可。
下载命令如下：

pip install pyecharts

代码中引用示例：

import pyecharts.options as opts
from pyecharts.charts import Line

2.查看官方示例

pyecharts-gallery，可通过官方示例详细学习和使用。

三、开发示例

下面我们就是用上面的JSON和pyecharts进行实践，生成一个折线图，且带有工具栏。通过浏览器访问html文件可查看统计图。例如：

可通过工具栏中下载图片，切换柱状图，折线图，数据等。

代码示例如下：

echarts/__init__.pydata.txtjsonData.py# 运行后生成line.htmlline.htmlline.py

jsonData.py代码不变
line.py

import jsonimport pyecharts.options as opts
from pyecharts.charts import Line
import jsonDatadate_json,genome_json,industrial_json,literature_json,patent_json = jsonData.formatData()
date = json.loads(date_json)["date"]
(Line().add_xaxis(xaxis_data=date).add_yaxis(series_name="genome",y_axis=json.loads(genome_json),symbol="emptyCircle",is_symbol_show=True,label_opts=opts.LabelOpts(is_show=True)).add_yaxis(series_name="industrial",y_axis=json.loads(industrial_json),symbol="emptyCircle",is_symbol_show=True,label_opts=opts.LabelOpts(is_show=True)).add_yaxis(series_name="patent",y_axis=json.loads(patent_json),symbol="emptyCircle",is_symbol_show=True,label_opts=opts.LabelOpts(is_show=True)).add_yaxis(series_name="literature",y_axis=json.loads(literature_json),symbol="emptyCircle",is_symbol_show=True,label_opts=opts.LabelOpts(is_show=True)).set_global_opts(title_opts=opts.TitleOpts(title="数统计", subtitle="纯属虚构"),tooltip_opts=opts.TooltipOpts(trigger="axis"),toolbox_opts=opts.ToolboxOpts(is_show=True),xaxis_opts=opts.AxisOpts(type_="category"),yaxis_opts=opts.AxisOpts(type_="value",splitline_opts=opts.SplitLineOpts(is_show=True),)).render("D:/test/demo/echarts/line.html")
)

运行line.py之后生成line.html文件，直接浏览器打开，可以看到如图：