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 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