目录

---

1 项目背景

2019年底，肺炎（COVID-19）定制网站在全球爆发，定制网站后来被确认为新型冠状病毒（SARS-CoV-2）所引发的。

2 项目目标

定制网站我们在爬取到公开数据的条件下，定制网站开展了一些可视化工作定制网站希望能够帮助大家更好理解现在疫情的发展情况，更有信心一起战胜肆虐的病毒。

3 项目分析

3.1数据获取

3.1.1分析网站

先去先找到今天要爬取的目标数据：

3.1.2找到数据所在url

url='https://api.inews.qq.com/newsqa/v1/query/inner/publish/modules/list?modules=statisGradeCityDetail,diseaseh5Shelf'

3.1.3获取数据

通过爬虫获取它的json数据：

url='https://api.inews.qq.com/newsqa/v1/query/inner/publish/modules/list?modules=statisGradeCityDetail,diseaseh5Shelf'
 
response = requests.get(url, verify=False)
 
json_data = response.json()['data']
 
china_data = json_data['diseaseh5Shelf']['areaTree'][0]['children'] # 列表

3.1.4解析数据

通过一个for循环对我们的列表进行取值然后再存入到我们的字典中

data_set = []
 
for i in china_data:
 
 data_dict = {}
 
 # 地区名称
 
 data_dict['province'] = i['name']
 
 # 新增确认
 
 data_dict['nowConfirm'] = i['total']['nowConfirm']
 
 # 死亡人数
 
 data_dict['dead'] = i['total']['dead']
 
    # 治愈人数
 
  data_dict['heal'] = i['total']['heal']
 
 
 
data_set.append(data_dict)

3.1.5保存数据

df = pd.DataFrame(data_set)

df.to_csv('yiqing_data.csv')

3.2

3.2.1读取数据

df2 = df.sort_values(by=['nowConfirm'],ascending=False)[:9]

df2

3.2.2各地区确诊人数与死亡人数情况条形图

  

bar = (
 
    Bar()
 
    .add_xaxis(list(df['province'].values)[:6])
 
    .add_yaxis("死亡", df['dead'].values.tolist()[:6])
 
    .add_yaxis("治愈", df['heal'].values.tolist()[:6])
 
    .set_global_opts(
 
        title_opts=opts.TitleOpts(title="各地区确诊人数与死亡人数情况"),
 
        datazoom_opts=[opts.DataZoomOpts()],
 
        )
 
)
 
bar.render_notebook()

3.2.3各地区现有确诊人数地图

china_map = (
 
    Map()
 
    .add("现有确诊", [list(i) for i in zip(df['province'].values.tolist(),df['nowConfirm'].values.tolist())], "china")
 
    .set_global_opts(
 
        title_opts=opts.TitleOpts(title="各地区确诊人数"),
 
        visualmap_opts=opts.VisualMapOpts(max_=600, is_piecewise=True),
 
    )
 
)
 
china_map.render_notebook()

---

3.2.4各地区现有确诊人数分布环形图

pie = (
 
    Pie()
 
    .add(
 
        "",
 
        [list(i) for i in zip(df2['province'].values.tolist(),df2['nowConfirm'].values.tolist())],
 
        radius = ["10%","30%"]
 
    )
 
    .set_global_opts(
 
            legend_opts=opts.LegendOpts(orient="vertical", pos_top="70%", pos_left="70%"),
 
    )
 
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
 
)
 
pie.render_notebook()

  

3.2.4各地区现有确诊人数分布折线图

line = (
 
    Line()
 
    .add_xaxis(list(df['province'].values))
 
    .add_yaxis("治愈", df['heal'].values.tolist())
 
    .add_yaxis("死亡", df['dead'].values.tolist())
 
    .set_global_opts(
 
        title_opts=opts.TitleOpts(title="死亡与治愈"),
 
 
 
    )
 
)
 
line.render_notebook()

项目源码：

import requests      # 发送网络请求模块
import json
import pprint        # 格式化输出模块
import pandas as pd  # 数据分析当中一个非常重要的模块
from pyecharts import options as opts
from pyecharts.charts import Bar,Line,Pie,Map,Grid
import urllib3
from pyecharts.globals import CurrentConfig, NotebookType
# 配置对应的环境类型
CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_NOTEBOOK
CurrentConfig.ONLINE_HOST='https://assets.pyecharts.org/assets/'
 
urllib3.disable_warnings()#解决InsecureRequestWarning: Unverified HTTPS request is being made to host 'api.inews.qq.com'. 问题
 
 
url = 'https://api.inews.qq.com/newsqa/v1/query/inner/publish/modules/list?modules=statisGradeCityDetail,diseaseh5Shelf'
 
response = requests.get(url, verify=False)
json_data = response.json()['data']
 
 
china_data = json_data['diseaseh5Shelf']['areaTree'][0]['children'] # 列表
 
data_set = []
for i in china_data:
    data_dict = {}
    # 地区名称
    data_dict['province'] = i['name']
    # 新增确认
    data_dict['nowConfirm'] = i['total']['nowConfirm']
    # 死亡人数
    data_dict['dead'] = i['total']['dead']
    # 治愈人数
    data_dict['heal'] = i['total']['heal']
 
    data_set.append(data_dict)
df = pd.DataFrame(data_set)
df.to_csv('yiqing_data.csv')
 
 
df2 = df.sort_values(by=['nowConfirm'],ascending=False)[:9]
df2
 
 
 
# bar = (
#     Bar()
#     .add_xaxis(list(df['province'].values)[:6])
#     .add_yaxis("死亡", df['dead'].values.tolist()[:6])
#     .add_yaxis("治愈", df['heal'].values.tolist()[:6])
#     .set_global_opts(
#         title_opts=opts.TitleOpts(title="各地区确诊人数与死亡人数情况"),
#         datazoom_opts=[opts.DataZoomOpts()],
#         )
# )
# bar.render_notebook()
# china_map = (
#     Map()
#     .add("现有确诊", [list(i) for i in zip(df['province'].values.tolist(),df['nowConfirm'].values.tolist())], "china")
#     .set_global_opts(
#         title_opts=opts.TitleOpts(title="各地区确诊人数"),
#         visualmap_opts=opts.VisualMapOpts(max_=600, is_piecewise=True),
#     )
# )
# china_map.render_notebook()
 
# pie = (
#     Pie()
#     .add(
#         "",
#         [list(i) for i in zip(df2['province'].values.tolist(),df2['nowConfirm'].values.tolist())],
#         radius = ["10%","30%"]
#     )
#     .set_global_opts(
#             legend_opts=opts.LegendOpts(orient="vertical", pos_top="70%", pos_left="70%"),
#     )
#     .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
# )
# pie.render_notebook()
line = (
    Line()
    .add_xaxis(list(df['province'].values))
    .add_yaxis("治愈", df['heal'].values.tolist())
    .add_yaxis("死亡", df['dead'].values.tolist())
    .set_global_opts(
        title_opts=opts.TitleOpts(title="死亡与治愈"),
 
    )
)
line.render_notebook()