一、前言

数据存储在网页上，需要爬取数据下来，数据存储格式是JSON，数据可视化在工作中也变得日益重要，接下来将数据爬虫与数据可视化结合起来，做个案例

注：当时数据是22年1月29日爬取数据

二、使用步骤

# 1.导入包
import pandas as pd 
import requests
import json



# 2.数据爬取
china_url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'

headers = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.114 Safari/537.36"
}
response = requests.get(url=china_url,headers = headers).json()
data = json.loads(response['data'])

with open('1-29.json','w',encoding='utf-8') as f: 
    f.write(json.dumps(data))



# 3.存储到dataframe中
# 获取中国所有的数据
chinaAreaDict=data['areaTree'][0]
# 获取 所有省份的数据 
provinceList = chinaAreaDict['children']

'''
[{河南，安阳}，{河南，许昌}], 为了方便存储到dataframe中
'''
china_cityList = []
# 遍历每一个省份数据
for x in range(len(provinceList)):

    # 省份的名称
    province = provinceList[x]['name']
    
    # 获取每一个省下的所有的城市
    province_list = provinceList[x]['children']
    
    # 遍历这个省份的所有城市【一个一个的拿出来】
    for y in range(len(province_list)):
        # 获取城市的名字
        city = province_list[y]['name']
        # 获取当前城市的总数据
        total = province_list[y]['total']
        # 获取当前城市的今日数据
        today = province_list[y]['today']
        # 保存成一个字典 {'province':province, 'city':city, 'total':total}
        china_dict = {'province':province, 'city':city, 'total':total, 'today':today}
        
        # 添加到列表中
        china_cityList.append(china_dict)

# 将放有每一个城市的列表构建成dataframe
chinaTotalData = pd.DataFrame(china_cityList)
chinaTotalData




# 4.将total列数据字典转化成多列
total_dataframe = pd.DataFrame()

def func(ser):
    global total_dataframe
    total_dataframe = total_dataframe.append(pd.Series(ser['total']).to_frame().T,ignore_index = True)

chinaTotalData.apply(func,axis=1)

# 修改列的名字
def rename_func(x):
    return "total_" + x   # total_nowConfirm

total_dataframe.rename(columns = rename_func, inplace = True)

chinaTotalData = pd.merge(chinaTotalData,total_dataframe,left_index=True,right_index=True,how='outer')
# 删除total列
chinaTotalData.drop(['total'],axis=1, inplace = True)
chinaTotalData






# 5.将today列数据字典转化成多列
total_dataframe = pd.DataFrame()

def func(ser):
    global total_dataframe
    total_dataframe = total_dataframe.append(pd.Series(ser['today']).to_frame().T,ignore_index = True)

chinaTotalData.apply(func,axis=1)

# 修改列的名字
def rename_func(x):
    return "today_" + x   # total_nowConfirm

total_dataframe.rename(columns = rename_func, inplace = True)

chinaTotalData = pd.merge(chinaTotalData,total_dataframe,left_index=True,right_index=True,how='outer')
# 删除total列
chinaTotalData.drop(['today'],axis=1, inplace = True)
chinaTotalData





# 6.保存到Excel
chinaTotalData.to_excel('1-29疫情数据.xlsx',sheet_name='数据',index=False)





# 7.根据数据绘制国内确诊人数总疫情图
from pyecharts.charts import Map  # 导入地图对象
import pyecharts


# 整理数据格式，满足地图的数据要求
data = chinaTotalData.groupby(by='province',as_index = False).sum()

data_list = list(zip(data['province'].values.tolist(),data['total_confirm'].values.tolist()))


# 创建地图对象
map_china = Map()


map_china.add(series_name='确诊病例数',data_pair=data_list)

map_china.set_global_opts(title_opts=opts.TitleOpts(title="疫情地图"),
                         visualmap_opts=opts.VisualMapOpts(
                                                                          is_piecewise=True, # 是否分段
                                                                          pieces=[{"max": 9, "min": 0, "label": "0-9","color":"#FFE4E1"},
                                                                                      {"max": 99, "min": 10, "label": "10-99","color":"#FF7F50"},
                                                                                      {"max": 499, "min": 100, "label": "100-499","color":"#F08080"},
                                                                                      {"max": 999, "min": 500, "label": "500-999","color":"#CD5C5C"},
                                                                                      {"max": 9999, "min": 1000, "label": "1000-9999", "color":"#990000"},
                                                                                      {"max": 99999, "min": 10000, "label": ">=10000", "color":"#660000"}]
                         
                         )
                         )
map_china.render_notebook()

---

数据展示

1.原始数据（Json）与结果集（Excel）所示：

2.数据地图可视化所示（当时的记录）：
（1）、

（2）、

（3）、