python绘图pyecharts+pandas的使用详解
作者:ElTarget 时间:2022-02-03 18:00:44
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
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()
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()
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()
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()
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()
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 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
# 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
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
来源:https://blog.csdn.net/qq_33511315/article/details/108173301
标签:pyecharts,pandas,使用
![](/images/zang.png)
![](/images/jiucuo.png)
猜你喜欢
Google logo “我的中国”谷歌国际少年绘画大赛小学1-3年级
2008-12-19 12:26:00
![](https://img.aspxhome.com/file/UploadPic/200812/19/j1-3_01-72s.jpg)
php+html5基于websocket实现聊天室的方法
2023-11-15 06:58:58
正则的replace方法(产生的字符串副本)
2008-06-03 13:31:00
如何利用Python监控别人的网站
2022-08-11 16:54:18
Python ZipFile模块详解
2021-09-17 06:30:24
OpenCV 基本图形绘制函数详解
2022-01-22 11:09:59
![](https://img.aspxhome.com/file/2023/6/96456_0s.png)
史上最简单的方法复制或迁移Oracle数据库
2009-02-04 16:38:00
python自动化测试之如何解析excel文件
2022-08-28 08:24:36
PHP session会话的安全性分析
2023-11-21 23:47:59
![](https://img.aspxhome.com/file/2023/5/98075_0s.gif)
《HTML5设计原理》读后随记
2011-01-25 12:26:00
也谈谈DIV+CSS的牛角尖
2007-11-16 16:12:00
网页版面布局的方法及技巧
2007-10-29 12:41:00
python 文本单词提取和词频统计的实例
2022-10-25 04:53:03
浅析php中array_map和array_walk的使用对比
2023-09-10 22:22:28
通过实例解析Python RPC实现原理及方法
2022-06-19 00:50:38
![](https://img.aspxhome.com/file/2023/7/70177_0s.png)
该用多大的字
2009-05-17 14:39:00
python deque模块简单使用代码实例
2022-10-16 04:08:12
js放大缩小容器:仿动画
2008-02-15 11:34:00
详谈python3中用for循环删除列表中元素的坑
2023-08-01 06:04:31
Python处理字符串的常用函数实例总结
2022-10-19 21:09:39
![](https://img.aspxhome.com/file/2023/5/75175_0s.png)