Under the condition of crawling public data, we have carried out some visualization work, hoping to help everyone better understand the current development of the epidemic, and have more confidence to defeat the raging virus together.

3 Item Analysis

3.1 Data Acquisition

3.1.1 Analysis Website

Go first and find the target data to be crawled today:

https://news.qq.com/zt2020/page/feiyan.htm#/

3.1.2 Find the url where the data is located

urlClick to jump to view

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

3.1.3 Get data

Get its json data through a crawler:


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 Analyzing data

Value our list through a for loop and store it in our dictionary


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 Saving data

df = pd.DataFrame(data_set)

df.to_csv('yiqing_data.csv')

3.2 Data Visualization

3.2.1 Read data

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

df2

3.2.2 Bar chart of the number of confirmed cases and the number of deaths in each region


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 Map of the number of confirmed cases in each region


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 Ring diagram of the distribution of the number of confirmed cases in each region


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 Line chart of the distribution of the number of confirmed cases in each region


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()

Project source code:


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()