数据爬取

数据爬取

使用Python来爬取数据，需要在pycharm中下载相应的插件。

import time
import json
import requests
from datetime import datetime
import pandas as pd
import numpy as np

def catch_data():
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
    reponse = requests.get(url=url).json()
    #返回数据字典
    data = json.loads(reponse['data'])
    return data

data = catch_data()
data.keys()
#print("字典所有的键为: %s" % data.keys())

# 数据集包括["国内总量","国内新增","更新时间","数据明细","每日数据","每日新增"]

lastUpdateTime = data['lastUpdateTime']
chinaTotal = data['chinaTotal']
chinaAdd = data['chinaAdd']
#print(chinaTotal)
#print(chinaAdd)



# 数据明细，数据结构比较复杂，一步一步打印出来看，先明白数据结构
areaTree = data['areaTree']
# 国内数据
china_data = areaTree[0]['children']
china_list = []
for a in range(len(china_data)):
    province = china_data[a]['name']
    province_list = china_data[a]['children']
    for b in range(len(province_list)):
        city = province_list[b]['name']
        total = province_list[b]['total']
        today = province_list[b]['today']
        china_dict = {}
        china_dict['province'] = province
        china_dict['city'] = city
        china_dict['total'] = total
        china_dict['today'] = today
        china_list.append(china_dict)

china_data = pd.DataFrame(china_list)
china_data.head()

# 定义数据处理函数
def confirm(x):
    confirm = eval(str(x))['confirm']
    return confirm
def suspect(x):
    suspect = eval(str(x))['suspect']
    return suspect
def dead(x):
    dead = eval(str(x))['dead']
    return dead
def heal(x):
    heal =  eval(str(x))['heal']
    return heal


# 函数映射
china_data['confirm'] = china_data['total'].map(confirm)
china_data['suspect'] = china_data['total'].map(suspect)
china_data['dead'] = china_data['total'].map(dead)
china_data['heal'] = china_data['total'].map(heal)
china_data['addconfirm'] = china_data['today'].map(confirm)
china_data['addsuspect'] = china_data['today'].map(confirm)
china_data['adddead'] = china_data['today'].map(confirm)
china_data['addheal'] = china_data['today'].map(confirm)
china_data = china_data[["province","city","confirm","suspect","dead","heal","addconfirm","addsuspect","adddead","addheal"]]
china_data.head()


global_data = pd.DataFrame(data['areaTree'])
global_data['confirm'] = global_data['total'].map(confirm)
global_data['suspect'] = global_data['total'].map(suspect)
global_data['dead'] = global_data['total'].map(dead)
global_data['heal'] = global_data['total'].map(heal)
global_data['addconfirm'] = global_data['today'].map(confirm)
global_data['addsuspect'] = global_data['today'].map(confirm)
global_data['adddead'] = global_data['today'].map(confirm)
global_data['addheal'] = global_data['today'].map(confirm)
#world_name = pd.read_excel("世界各国中英文对照.xlsx")
#global_data = pd.merge(global_data,world_name,left_on ="name",right_on = "中文",how="inner")
#global_data = global_data[["name","英文","confirm","suspect","dead","heal","addconfirm","addsuspect","adddead","addheal"]]
#global_data.head()


chinaDayList = pd.DataFrame(data['chinaDayList'])
#chinaDayList = chinaDayList[['date','confirm','suspect','dead','heal']]
chinaDayList.head()


chinaDayAddList = pd.DataFrame(data['chinaDayAddList'])
#chinaDayAddList = chinaDayAddList[['date','confirm','suspect','dead','heal']]
chinaDayAddList.head()

from pyecharts.charts import * #导入所有图表
from pyecharts import options as opts
#导入pyecharts的主题（如果不使用可以跳过）
from pyecharts.globals import ThemeType

total_pie = Pie(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS,width = '500px',height ='350px'))  #设置主题，和画布大小
total_pie.add("",[list(z) for z in zip(chinaTotal.keys(), chinaTotal.values())],
            center=["50%", "50%"], #图的位置
            radius=[50, 80])   #内外径大小
total_pie.set_global_opts(
            title_opts=opts.TitleOpts(title="全国总量",subtitle=("截止"+lastUpdateTime)))
total_pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{c}"))  #标签格式
total_pie.render_notebook()


totaladd_pie = Pie(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS,width = '500px',height ='350px'))  #设置主题，和画布大小
totaladd_pie.add("",[list(z) for z in zip(chinaAdd.keys(), chinaAdd.values())],
            center=["50%", "50%"],
            radius=[50, 80])
totaladd_pie.set_global_opts(
            title_opts=opts.TitleOpts(title="昨日新增"))
totaladd_pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{c}"))  #标签格式
totaladd_pie.render_notebook()


world_map = Map(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS))
world_map.add("",[list(z) for z in zip(list(global_data["confirm"]), list(global_data["confirm"]))], "world",is_map_symbol_show=False)
world_map.set_global_opts(title_opts=opts.TitleOpts(title="2019_nCoV-世界疫情地图"),
                          visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
                          pieces = [
                        {"min": 101 , "label": '>100'}, #不指定 max，表示 max 为无限大
                        {"min": 10, "max": 100, "label": '10-100'},
                        {"min": 0, "max": 9, "label": '0-9' }]))
world_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
world_map.render_notebook()

world_map = Map(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS))
world_map.add("",[list(z) for z in zip(list(global_data["confirm"]), list(global_data["confirm"]))], "world",is_map_symbol_show=False)
world_map.set_global_opts(title_opts=opts.TitleOpts(title="2019_nCoV-世界疫情地图"),
                          visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
                          pieces = [
                        {"min": 101 , "label": '>100'}, #不指定 max，表示 max 为无限大
                        {"min": 10, "max": 100, "label": '10-100'},
                        {"min": 0, "max": 9, "label": '0-9' }]))
world_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
world_map.render_notebook()

#数据处理
area_data = china_data.groupby("province")["confirm"].sum().reset_index()
area_data.columns = ["province","confirm"]
area_map = Map(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS))
area_map.add("",[list(z) for z in zip(list(area_data["province"]), list(area_data["confirm"]))], "china",is_map_symbol_show=False)
area_map.set_global_opts(title_opts=opts.TitleOpts(title="2019_nCoV中国疫情地图"),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
                pieces = [
                        {"min": 1001 , "label": '>1000',"color": "#893448"}, #不指定 max，表示 max 为无限大
                        {"min": 500, "max": 1000, "label": '500-1000',"color": "#ff585e"},
                        {"min": 101, "max": 499, "label": '101-499',"color": "#fb8146"},
                        {"min": 10, "max": 100, "label": '10-100',"color": "#ffb248"},
                        {"min": 0, "max": 9, "label": '0-9',"color" : "#fff2d1" }]))
area_map.render_notebook()

line1 = Line(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS))
#line1.add_xaxis(list(chinaDayList["date"]))
#line1.add_yaxis("治愈",list(chinaDayList["heal"]),is_smooth=True)
#line1.add_yaxis("死亡", list(chinaDayList["dead"]),is_smooth=True)
#line1.set_global_opts(title_opts=opts.TitleOpts(title="Line1-治愈与死亡趋势"))
#line1.render_notebook()


line2 = Line(init_opts=opts.InitOpts(theme=ThemeType.SHINE))
#line2.add_xaxis(list(chinaDayList["date"]))
#line2.add_yaxis("确诊",list(chinaDayList["confirm"]))
#line2.add_yaxis("疑似", list(chinaDayList["suspect"]))
#line2.set_global_opts(title_opts=opts.TitleOpts(title="Line2-确诊与疑似趋势"))
#line2.render_notebook()


bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS,width = '900px',height ='400px'))
#bar .add_xaxis(list(chinaDayAddList["date"]))
#bar .add_yaxis("确诊", list(chinaDayAddList["confirm"]))
#bar .add_yaxis("疑似", list(chinaDayAddList["suspect"]))
#bar .add_yaxis("死亡", list(chinaDayAddList["dead"]))
#bar .add_yaxis("治愈", list(chinaDayAddList["heal"]))
#bar .set_global_opts(title_opts=opts.TitleOpts(title="每日新增数据趋势"))
#bar.render_notebook()


page = Page()
page.add(total_pie)
page.add(totaladd_pie)
page.add(world_map)
page.add(area_map)
page.add(line1)
page.add(line2)
page.add(bar)
page.render('files/show.html')




Show.html 
<!DOCTYPE html>
<html>
<head>
    <meta charset="UTF-8">
    <title>Awesome-pyecharts</title>
            <script type="text/javascript" src="https://assets.pyecharts.org/assets/echarts.min.js"></script>
        <script type="text/javascript" src="https://assets.pyecharts.org/assets/themes/westeros.js"></script>
        <script type="text/javascript" src="https://assets.pyecharts.org/assets/maps/world.js"></script>
        <script type="text/javascript" src="https://assets.pyecharts.org/assets/maps/china.js"></script>
        <script type="text/javascript" src="https://assets.pyecharts.org/assets/themes/shine.js"></script>

    
</head>
<body>
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                    "value": 107
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                    "value": 295
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                    "value": 913
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            ],
            "radius": [
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    "title": [
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};
        chart_b5493c495c75481dafbd8aa5465feb69.setOption(option_b5493c495c75481dafbd8aa5465feb69);
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<br/>                <div id="022269587237463cb5386c7b2920fdbe" class="chart-container" style="500px; height:350px;"></div>
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<br/>                <div id="c1c974ee0ffe4b8fa0218a313dbe1900" class="chart-container" style="900px; height:500px;"></div>
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