pyecharts介绍

pyecharts 是一个用于生成 Echarts 图表的类库。Echarts 是百度开源的一个数据可视化 JS 库。用 Echarts 生成的图可视化效果非常棒

为避免绘制缺漏，建议全部安装

为了避免下载缓慢，作者全部使用镜像源下载过了

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-countries-pypkgpip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-provinces-pypkgpip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-cities-pypkgpip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-counties-pypkgpip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-misc-pypkgpip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-united-kingdom-pypkg

基础案例

from pyecharts.charts import Barbar = Bar()bar.add_xaxis(['小嘉','小琪','大嘉琪','小嘉琪'])bar.add_yaxis('得票数',[60,60,70,100])#render会生成本地HTML文件，默认在当前目录生成render.html# bar.render()#可以传入路径参数，如 bar.render("mycharts.html")#可以将图形在jupyter中输出，如 bar.render_notebook()bar.render_notebook()from pyecharts.charts import Barfrom pyecharts import options as opts# 示例数据cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']data1 = [123, 153, 89, 107, 98, 23]data2 = [56, 77, 93, 68, 45, 67]# 1.x版本支持链式调用bar = (Bar() .add_xaxis(cate) .add_yaxis('渠道', data1) .add_yaxis('门店', data2) .set_global_opts(title_opts=opts.TitleOpts(title="示例", subtitle="副标")) )bar.render_notebook()from pyecharts.charts import Piefrom pyecharts import options as opts# 示例数据cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']data = [153, 124, 107, 99, 89, 46]pie = (Pie() .add('', [list(z) for z in zip(cate, data)], radius=["30%", "75%"], rosetype="radius") .set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本示例", subtitle="我是副标题")) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%")) )pie.render_notebook()from pyecharts.charts import Linefrom pyecharts import options as opts# 示例数据cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']data1 = [123, 153, 89, 107, 98, 23]data2 = [56, 77, 93, 68, 45, 67]"""折线图示例:1. is_smooth 折线 OR 平滑2. markline_opts 标记线 OR 标记点"""line = (Line() .add_xaxis(cate) .add_yaxis('电商渠道', data1, markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")])) .add_yaxis('门店', data2, is_smooth=True, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="自定义标记点", coord=[cate[2], data2[2]], value=data2[2])])) .set_global_opts(title_opts=opts.TitleOpts(title="Line-基本示例", subtitle="我是副标题")) )line.render_notebook()from pyecharts import options as optsfrom pyecharts.charts import Geofrom pyecharts.globals import ChartTypeimport randomprovince = ['福州市', '莆田市', '泉州市', '厦门市', '漳州市', '龙岩市', '三明市', '南平']data = [(i, random.randint(200, 550)) for i in province]geo = (Geo() .add_schema(maptype="福建") .add("门店数", data, type_=ChartType.HEATMAP) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts( visualmap_opts=opts.VisualMapOpts(), legend_opts=opts.LegendOpts(is_show=False), title_opts=opts.TitleOpts(title="福建热力地图")) )geo.render_notebook()

啊哈这个还访问不了哈

ImportError: Missing optional dependency ‘xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd.

20200822pyecharts+pandas 初步学习

作者今天学习做数据分析，有错误请指出
下面贴出源代码

# 获取数据import requestsimport jsonchina_url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'#foreign_url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_foreign'headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36 Edg/84.0.522.59', 'referer': 'https://news.qq.com/zt2020/page/feiyan.htm'}#获取json数据response = requests.get(url=china_url,headers=headers).json()print(response)#先将json数据转 python的字典data = json.loads(response['data'])#保存数据 这里使用encoding='utf-8' 是因为作者想在jupyter上面看with open('./国内疫情.json','w',encoding='utf-8') as f: #再将python的字典转json数据 # json默认中文以ASCII码显示 在这里我们以中文显示 所以False #indent=2：开头空格2 f.write(json.dumps(data,ensure_ascii=False,indent=2))

转换为json格式输出的文件

# 将json数据转存到Excel中import pandas as pd#读取文件with open('./国内疫情.json',encoding='utf-8') as f: data = f.read() #将数据转为python数据格式data = json.loads(data)type(data)#字典类型lastUpdateTime = data['lastUpdateTime']#获取中国所有数据chinaAreaDict = data['areaTree'][0]#获取省级数据provinceList = chinaAreaDict['children']# 获取的数据有几个省市和地区print('数据共有：',len(provinceList),'省市和地区')#将中国数据按城市封装，例如【{湖北，武汉}，{湖北，襄阳}】，为了方便放在dataframe中china_citylist = []for x in range(len(provinceList)): # 每一个省份的数据 province =provinceList[x]['name'] #有多少个市 province_list = provinceList[x]['children'] for y in range(len(province_list)): # 每一个市的数据 city = province_list[y]['name'] # 累积所有的数据 total = province_list[y]['total'] # 今日的数据 today = province_list[y]['today'] china_dict = {'省份':province, '城市':city, 'total':total, 'today':today } china_citylist.append(china_dict)chinaTotaldata = pd.DataFrame(china_citylist)nowconfirmlist=[]confirmlist=[]suspectlist=[]deadlist=[]heallist=[]deadRatelist=[]healRatelist=[]# 将整体数据chinaTotaldata的数据添加dataframefor value in chinaTotaldata['total'] .values.tolist():#转成列表 confirmlist.append(value['confirm']) suspectlist.append(value['suspect']) deadlist.append(value['dead']) heallist.append(value['heal']) deadRatelist.append(value['deadRate']) healRatelist.append(value['healRate']) nowconfirmlist.append(value['nowConfirm']) chinaTotaldata['现有确诊']=nowconfirmlist chinaTotaldata['累计确诊']=confirmlistchinaTotaldata['疑似']=suspectlistchinaTotaldata['死亡']=deadlistchinaTotaldata['治愈']=heallistchinaTotaldata['死亡率']=deadRatelistchinaTotaldata['治愈率']=healRatelist#拆分today列today_confirmlist=[]today_confirmCutlist=[]for value in chinaTotaldata['today'].values.tolist(): today_confirmlist.append(value['confirm']) today_confirmCutlist.append(value['confirmCuts'])chinaTotaldata['今日确诊']=today_confirmlistchinaTotaldata['今日死亡']=today_confirmCutlist#删除total列 在原有的数据基础chinaTotaldata.drop(['total','today'],axis=1,inplace=True)# 将其保存到excel中from openpyxl import load_workbookbook = load_workbook('国内疫情.xlsx')# 避免了数据覆盖writer = pd.ExcelWriter('国内疫情.xlsx',engine='openpyxl')writer.book = bookwriter.sheets = dict((ws.title,ws) for ws in book.worksheets)chinaTotaldata.to_excel(writer,index=False)writer.save()writer.close()chinaTotaldata

作者这边还有国外的，不过没打算分享出来，大家就看看，总的来说我们国内情况还是非常良好的

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