python 数据可视化大屏制作

 from pyecharts import options as opts

    
 from pyecharts.globals import ThemeType #导入主题库
    
 from pyecharts.globals import SymbolType #主题库
    
 from pyecharts.charts import Pie,Bar,Map,WordCloud,Page  #玫瑰图，柱状图，地图，词云，组合图
    
 import pandas as pd
    
    
    
    
    代码解读

 df_taobao = pd.read_excel("D:/可视化项目/数据可视化演示/taobao_goods.xlsx") #读取数据

    
 df_taobao
    
    
    
    
    代码解读

 #删除重复值

    
 df_taobao.drop_duplicates(inplace=True)  
    
    
    
    
    代码解读

 #提取省份

    
 ld_list = []
    
 for ld in df_taobao["location"]:
    
     ld_list.append(ld.split(" ")[0])  #使用分割提取
    
     
    
 #原数据表中替换省份    
    
 df_taobao["location"] = ld_list
    
 df_taobao
    
    
    
    
    代码解读

 #销售数据格式转换

    
 sales_list = []
    
 for sale in df_taobao["sales"]:
    
     sale = sale[:-3].replace("+","")  #去掉“人付款”三字
    
     if "万" in sale:
    
     sale = int(float(sale[:-1])*10000) #将万转换为元
    
     print(sale)
    
     sales_list.append(sale)
    
 df_taobao["sales"] = sales_list
    
    
    
    
    代码解读

 locations = [location for location in df_taobao["location"].value_counts().items()]  #提取省份和数量使用items组成元组

    
 locations
    
    
    
    
    代码解读

 #地图

    
 map = (
    
 Map()
    
     .add("店铺数量",[list(location) for location in locations],"china")  #显示店铺数量，使用china地图
    
     .set_global_opts(
    
     title_opts = opts.TitleOpts(title = "地址分布图"), #设置标题
    
      visualmap_opts = opts.VisualMapOpts(max_ = 3000),  #设置阈值
    
     
    
     )
    
 )
    
 map.render() #保存为html
    
 map.render_notebook()  #在notebook中显示图像
    
    
    
    
    代码解读

 #用户评论词云

    
 from wordcloud import WordCloud
    
 import numpy as np
    
 import matplotlib.pyplot as plt
    
 from PIL import Image
    
 import jieba 
    
 df_commments = pd.read_excel("D:/可视化项目/数据可视化演示/goods_comments.xlsx")
    
 df_commments
    
    
    
    
    代码解读

    text = "".join(jieba.lcut(str([i for i in df_commments["comment"]])))  #将所有评论组合成一个text
    
    代码解读

 mask =np.array( Image.open("D:/可视化项目/数据可视化演示/bra.jpg"))  #词云背景图片

    
 wc = WordCloud(mask=mask,font_path='C:/Windows/Fonts/simsun.ttc',mode="RGBA").generate(text) #设置格式
    
 plt.imshow(wc,interpolation="bilinear") #显示词云
    
 wc.to_file("词.png")  #保存
    
    
    
    
    代码解读

 from pyecharts.faker import Faker  #假数据库

    
 bar = (
    
     Bar()
    
     .add_xaxis(Faker.days_attrs)
    
     .add_yaxis("商家A",Faker.days_values)
    
     .add_yaxis("商家B",Faker.days_values)
    
     .add_yaxis("商家C",Faker.days_values)
    
     .set_global_opts(
    
     title_opts=opts.TitleOpts(title = "柱状图"),
    
     datazoom_opts = [opts.DataZoomOpts()] #设置动态图
    
     )
    
 )
    
 bar.render_notebook()
    
    
    
    
    代码解读

 from pyecharts import options as opts

    
 from pyecharts.charts import Pie
    
 from pyecharts.faker import Faker
    
  
    
 v = Faker.choose()
    
 pie =(
    
     Pie()
    
     .add(
    
     "",
    
     [list(z) for z in zip(v,list(range(10,1000,10)))],
    
     radius = ["30%","75%"],    #图像中心园的大小
    
 #     center=["75%","50%"],   显示在图像的区域
    
     
    
     rosetype = "radius",
    
     label_opts=opts.LabelOpts(is_show=False),
    
     )
    
     .set_global_opts(title_opts=opts.TitleOpts(title="玫瑰实列"))
    
 )
    
 pie.render_notebook()
    
    
    
    
    代码解读

 from pyecharts import options as opts

    
 from pyecharts.charts import Liquid
    
 liquid = (
    
     Liquid()
    
     .add("lq",[0.45,0.45])
    
     #第一个值显示数值，第二个是水位高度
    
     .set_global_opts(title_opts = opts.TitleOpts(title = "湿度"))
    
 )
    
 liquid.render_notebook()
    
    
    
    
    代码解读

 page = Page(layout=Page.DraggablePageLayout)

    
 page.add(
    
 bar,
    
 liquid,
    
 pie,
    
 map,
    
 )  #将写的单个图片全导入
    
 page.render_notebook()
    
 page.render() #保存组合图
    
 Page.save_resize_html("render.html",cfg_file="chart_config (1).json") #保存组合图为json格式
    
    
    
    
    代码解读