python从入门到精通：数据可视化-图形开发

1、json数据格式

• Json是一种轻量级的数据交互格式，可以按照Json指定的格式去组织和封装数据

• Json本质上是一种带有特殊格式的字符串

主要功能：json就是一种在各个编程语言中流通的数据格式，负责不同编程语言中的数据传递和交互，类似于：

• 国际通用语言-英语

• 中国不同名族的通用语言-中文（普通话） 

• 各种编程语言存储数据的容器不尽相同，在python中有字典dict这样的数据类型，而其它语言可能没有对应的字典。为了让不同的语言都能够相互通用的互相传递数据，Json就是一种非常良好的数据中转格式。如下图，以python和C语言相互传数据为例：

• json格式的数据要求很严格：

# json的数据格式可以是：

{"name":"admin","age":18}

# 也可以是：

[{"name":"admin","age":18},{"name":"root","age":16},{"name":"张三":"age":20}] 

• python与json数据的相互转换：

# json数据与python数据的相互转换：
import json
# 准备符合json格式要求的python数据
data = [{"name":"老王","age":36},{"name":"小白","age":18}]
# 通过json.dumps(data)将python数据转化为json数据
data = json.dumps(data, ensure_ascii=False)
print(data)
print(type(data))
# 通过json.loads(data)将json数据转化为python数据
data = json.loads(data)
print(data)
print(type(data))

2、pyecharts模块

• Echarts是由百度公司开源的数据可视化，凭借良好的交互性，精巧的图标设计，得到众多开发者认可。而python是门富有表达力的的语言，很适合用于数据处理，当数据分析遇上数据可视化时pyecharts就诞生了。

• 通过使用pip命令快速安装pyecharts模块

官方画廊网站：官方网址

3、pyecharts入门

• 基础折线图：

# 导包，导入Line功能构建折线图对象
from pyecharts.charts import Line
# 得到折线图对象
line = Line()
# 添加x轴数据
line.add_xaxis(["中国","美国","英国"])
line.add_yaxis("GDP",[30,20,10])
# 生成图表
line.render()

 • pyecharts模块中有很多配置选项，常用到两个类别的选项：

1、全局配置选项

2、系列配置选项

• 全局配置选项可以通过set_global_opts方法来进行配置，相应的选项和选线那个的功能：

 set_global_opts方法：

from pyecharts.charts import Line
from pyecharts.options import TitleOpts, LegendOpts, ToolboxOpts, VisualMapOptsline = Line()
line.add_xaxis(["小铭", "小红"])  # 添加x轴上信息
line.add_yaxis("资金", [10, 20])  # 添加y轴上信息
# 设置全局配置项
line.set_global_opts(title_opts=TitleOpts(title="测试", pos_left="center", pos_bottom="1%"),  # 标题配置项legend_opts=LegendOpts(is_show=True),  # 图例配置项toolbox_opts=ToolboxOpts(is_show=True),  # 提示框配置项visualmap_opts=VisualMapOpts(is_show=True)  # 视觉映射配置项
)
line.render()  # 得到一个html文件

4、数据处理

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首先对开头和结尾进行处理，去除不符合json格式的数据。然后登陆网站：官网 选择json视图，把内容粘贴进去即可看到层级关系。

5、创建折线图

import json
from pyecharts.charts import Line
from pyecharts.options import TitleOpts,LegendOpts,ToolboxOpts,VisualMapOpts,TooltipOpts,LabelOpts
# 读取数据
f_us = open('美国.txt',"r",encoding="utf-8")
data_us = f_us.read()
f_jp = open('日本.txt',"r",encoding="utf-8")
data_jp = f_jp.read()
f_in = open('印度.txt',"r",encoding="utf-8")
data_in = f_in.read()
# 去掉不合json格式的开头
data_us=data_us.replace("jsonp_1629344292311_69436(","")
data_jp=data_jp.replace("jsonp_1629350871167_29498(","")
data_in=data_in.replace("jsonp_1629350745930_63180(","")
# 去掉不合json格式的结尾
data_us=data_us[:-2]
data_jp=data_jp[:-2]
data_in=data_in[:-2]
# 将json格式的数据转化为python字典
us_dict=json.loads(data_us)
jp_dict=json.loads(data_jp)
in_dict=json.loads(data_in)
# 获取trend key
us_dict=us_dict["data"][0]["trend"]
jp_dict=jp_dict["data"][0]["trend"]
in_dict=in_dict["data"][0]["trend"]
# 获取日期数据，用于x轴，取2020年
us_x_data = us_dict["updateDate"][:314]
jp_x_data = jp_dict["updateDate"][:314]
in_x_data = in_dict["updateDate"][:314]
# 获取确诊数据，用于y轴，到2020年
us_y_data = us_dict["list"][0]["data"][0:314]
jp_y_data = jp_dict["list"][0]["data"][0:314]
in_y_data = in_dict["list"][0]["data"][0:314]
# 生成图表
line = Line()
line.add_xaxis(us_x_data) # x轴上信息共用
line.add_yaxis("美国疫情确诊数", us_y_data,label_opts=LabelOpts(is_show=False))
line.add_yaxis("日本疫情确诊数", jp_y_data,label_opts=LabelOpts(is_show=False))
line.add_yaxis("印度疫情确诊数", in_y_data,label_opts=LabelOpts(is_show=False))
line.set_global_opts(title_opts=TitleOpts(title="美国、日本、印度疫情分析", pos_left="center", pos_bottom="1%"),  # 标题配置项legend_opts=LegendOpts(is_show=True),  # 图例配置项toolbox_opts=ToolboxOpts(is_show=True),  # 提示框配置项visualmap_opts=VisualMapOpts(is_show=True)  # 视觉映射配置项
)
line.render()
f_us.close()
f_jp.close()
f_in.close()

6、创建地图

# from pyecharts.charts import Map
# from pyecharts.options import VisualMapOpts
# map = Map()
# data=[
#     ("上海市",10),
#     ("台湾省",20),
#     ("河南省",100),
#     ("北京市",120)
# ]
# map.add("测试地图",data,"china")
# map.set_global_opts(
#     visualmap_opts=VisualMapOpts(
#         is_show=True,
#         is_piecewise=True,
#         pieces=[
#             {"min":1,"max":9,"label":"1-9人","color":"#CCFFFF"},
#             {"min":10,"max":99,"label":"10-99人","color":"#FFFF99"},
#             {"min":100,"max":499,"label":"100-499人","color":"FF9966"},
#             {"min":500,"max":999,"label":"500-999人","color":"FF9966"},
#             {"min":1000,"max":9999,"label":"1000-9999人","color":"FF9966"},
#             {"min":10000,"label":"10000人以上","color":"FF9966"},
#         ]
#     )
# )
# map.render("map2.html")

国内疫情构建：

import json
from pyecharts.charts import Map
from pyecharts.options import VisualMapOpts,TitleOpts
f = open("疫情.txt","r",encoding="utf-8")
data=f.read()
data_dict=json.loads(data)
province_data_list = data_dict["areaTree"][0]["children"]
data_list=[]
for province_data in province_data_list:province_name = province_data["name"]if province_name  in "北京上海重庆天津":province_name += "市"else:province_name += "省"province_confirm = province_data["total"]["confirm"]data_list.append((province_name,province_confirm))
map = Map()
map.add("各地疫情确诊人数",data_list,"china")
map.set_global_opts(title_opts = TitleOpts(title="全国疫情地图"),visualmap_opts=VisualMapOpts(is_show=True,is_piecewise=True,pieces=[{"min":1,"max":99,"label":"1-99人","color":"#CCFFFF"},{"min":100,"max":999,"label":"100-999人","color":"#FFFF99"},{"min":1000,"max":4999,"label":"1000-4999人","color":"FF9966"},{"min":5000,"max":9999,"label":"5000-9999人","color":"FF6666"},{"min":10000,"max":99999,"label":"10000-99999人","color":"FF3333"},{"min":100000,"label":"100000人以上","color":"990033"},])
)
map.render("全国疫情地图.html")
f.close()

7、创建柱状图

柱状图构建：

from pyecharts.charts import Bar
from pyecharts.options import *
# 构建柱状图对象
bar = Bar()
# 添加x轴
bar.add_xaxis(["中国","美国","英国"])
# 添加y轴
bar.add_yaxis("GDP",[30,20,10],label_opts=LabelOpts(position="right"
))
# 反转x轴与y轴
bar.reversal_axis()
# 绘图
bar.render("bar_json.html")

创建时间线：

· Timeline( )-时间线

柱状图描述的是分类数据，回答的是每一个分类中有多少这个问题。这是柱状图的主要特点，同时柱状图很难动态地描述一个趋势性的数据。这里pyecharts为我们提供了一种解决方案-时间线

如果说一个Bar、Line对象是一张图表的话，时间线就是创建一个一维x轴，轴上每一个点就是一个图表对象。

# 创建时间线
from pyecharts.charts import Bar,Timeline
from pyecharts.options import *
from pyecharts.globals import ThemeType
bar1 = Bar()
bar1.add_xaxis(["中国","美国","英国"])
bar1.add_yaxis("GDP",[30,20,10],label_opts=LabelOpts(position="right"))
bar1.reversal_axis()
bar2 = Bar()
bar2.add_xaxis(["中国","美国","英国"])
bar2.add_yaxis("GDP",[90,70,50],label_opts=LabelOpts(position="right"))
bar2.reversal_axis()# 创建时间线对象,并设置主题
timeline = Timeline({"theme":ThemeType.LIGHT})
# timeline对象添加bar柱状图
timeline.add(bar1,"2021年GDP")
timeline.add(bar2,"2022年GDP")
# 设置自动播报
timeline.add_schema(play_interval=1000, # 自动播报的时间间隔，单位为毫秒is_time_line=True,  # 是否在自动播报的时间，显示时间线is_auto_play=True,  # 是否自动播报is_loop_play=True,  # 是否循环自动播报
)
# 通过时间线绘图
timeline.render()

列表的sort方法： 

在数据容器中学习过sorted函数，可以对数据容器进行排序。在后面的数据处理中，我们需要对列表进行排序，并指定排序规则，sorted函数就无法完成了。这个时候就用到sort方法：

列表.sort(key=选择排序依据的函数,reverse=True|False)

· 参数key，是要求传入一个函数，表示将列表的每一个元素都传入函数中，返回排序的依据

· 参数reverse，是否反转排序结果，True表示降序，False表示升序

# sort方法：
my_list = [["a",33],["b",55],["c",11]]
def choose_sort_key(element):return element[1]
my_list.sort(key=choose_sort_key,reverse=True)
print(my_list)

案例：实现1960-2019年各国GDP动态柱状图的变化趋势

# 导包
from pyecharts.charts import Bar,Timeline
from pyecharts.options import *
from pyecharts.globals import ThemeType
# 读取文件
f=open("1960-2019全球GDP数据.csv","r",encoding="GB2312")
data_lines=f.readlines()
f.close()
# 删除第一行数据
data_lines.pop(0)
# 将数据转化为字典
data_dict={}
for line in data_lines:year=int(line.split(",")[0]) # 每一行的的年份country=line.split(",")[1] # 每一行的国家GDP=float(line.split(",")[2]) # 每一行的gdp,用float来表示其中的科学计数法# 判断字典中是否有指定的keytry:data_dict[year].append([country,GDP])except KeyError:data_dict[year]=[]data_dict[year].append([country,GDP])
# 创建时间线对象
timeline=Timeline({"theme":ThemeType.LIGHT})
# 排序年份：取出全部key
sorted_year_list=sorted(data_dict.keys())
for year in sorted_year_list:data_dict[year].sort(key=lambda x:x[1],reverse=True)# 取出本年份GDP排行前八的国家year_data=data_dict[year][0:8]x_data=[]y_data=[]for country_gdp in year_data:x_data.append(country_gdp[0]) # x轴添加国家y_data.append(country_gdp[1] / 100000000) # y轴添加gdp#构建柱状图对象bar=Bar()x_data.reverse()y_data.reverse()bar.add_xaxis(x_data)bar.add_yaxis("GDP(亿)",y_data,label_opts=LabelOpts(position="right"))# 反转x、y轴bar.reversal_axis()# 设置每一年图表的标题bar.set_global_opts(title_opts=TitleOpts(title=f"{year}年全球GDP总量排行情况"))timeline.add(bar,str(year))
# 设置时间线自动播报
timeline.add_schema(play_interval=1000,is_timeline_show=True,is_auto_play=True,is_loop_play=False
)
# 绘图
timeline.render("1960-2019年各国GDP总量排行.html")