您现在的位置是：首页 > Python 当前栏目 python绘图pyecharts+pandas Python 数据 JSON JSON 2023-03-02 11:21:52 时间 ## 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-pypkg * pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-provinces-pypkg * pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-cities-pypkg * pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-counties-pypkg * pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-misc-pypkg * pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-united-kingdom-pypkg ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center] ## 基础案例 ## from pyecharts.charts import Bar bar = 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() ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 1] from pyecharts.charts import Bar from 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() ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 2] ```python from pyecharts.charts import Pie from 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() ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 3] from pyecharts.charts import Line from 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() ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 4] from pyecharts import options as opts from pyecharts.charts import Geo from pyecharts.globals import ChartType import random province = ['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() ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 5] ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 6] 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. ## ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 7] ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 8] ## 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 requests import json china_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 ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 9] # 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dataframe for 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']=confirmlist chinaTotaldata['疑似']=suspectlist chinaTotaldata['死亡']=deadlist chinaTotaldata['治愈']=heallist chinaTotaldata['死亡率']=deadRatelist chinaTotaldata['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_confirmlist chinaTotaldata['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_workbook book = 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 = book writer.sheets = dict((ws.title,ws) for ws in book.worksheets) chinaTotaldata.to_excel(writer,index=False) writer.save() writer.close() chinaTotaldata ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 10] ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 11] ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 12] ![在这里插入图片描述][20200822202232451.png_pic_center] 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 ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 13] [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center]: /images/20221124/6a97ae38c1a145288e760b227d99e9f7.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 1]: /images/20221124/abbb0cfceb6a4a059644a831544828ec.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 2]: /images/20221124/5cc2965a910f4c67b140f8034dc782ff.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 3]: /images/20221124/c7d610469516481db197344ac32fc9b9.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 4]: /images/20221124/b6f5aa43a31a4e4ba9fcc27c69658733.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 5]: /images/20221124/831ec7be9eba43a38a00cee41a32366f.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 6]: /images/20221124/c9ce28e2e7fb4579a0c7349cb05254e6.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 7]: /images/20221124/ff471a9af8cf496886b4f0d00f2a7faa.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 8]: /images/20221124/3f7c1cde26fb4b819a602a5b5b0393c9.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 9]: /images/20221124/d7862f43f8714ac490070ec8c19a82da.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 10]: /images/20221124/8541bafa98ed4558b5fbd7e6134d6f4d.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 11]: /images/20221124/d7b4fe5bd790429488271947460a1ba5.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 12]: /images/20221124/50c2e734125b4b8a9865715cf557c6e9.png [20200822202232451.png_pic_center]: /images/20221124/efc4c2f439274b42b55fd7a72bdaf20b.png [watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTExMzE1_size_16_color_FFFFFF_t_70_pic_center 13]: /images/20221124/bae2b438558c4d32a8d76f12c8388230.png 本文地址： python绘图pyecharts+pandas 相关文章 python 100练习 python open函数 python整数类型 防止python爬虫 torch.nonzero() python Python-装饰器 Amazon CodeGuru 新增功能 – Python 支持、安全检测器和内存分析 python列表复制 python基本语法 Python进程详解 Python 转 exe Python分式计算 python--file operater python3 定时器 python颜色设置 python-pyperclip模块 Python数据清洗 Python学习_6 python 简单绘图 Python字符串格式化
