pyecharts快速入门

发布时间：2024-11-30 23:02

编程语言速成课程快速入门 #生活技巧# #自我提升技巧# #技能提升课程#

pyecharts

pyecharts官网链接快速开始-pyecharts

全局设置

# 安装: pip install pyecharts from pyecharts.charts import Bar from pyecharts import options as opts # 内置主题类型可查看 pyecharts.globals.ThemeType from pyecharts.globals import ThemeType bar = ( Bar(init_opts=opts.InitOpts(width="1200px",height="500px",theme=ThemeType.DARK)) #黑色背景 .add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]) .add_yaxis("商家A", [5, 20, 36, 10, 75, 90]) .add_yaxis("商家B", [15, 6, 45, 20, 35, 66]) #设置全局配置项 .set_global_opts( title_opts=opts.TitleOpts(title="商家AB销售额对比",subtitle="副标题"),#大小标题 legend_opts=opts.LegendOpts(is_show=True), toolbox_opts=opts.ToolboxOpts(is_show=True), #可选择其他操作的窗口 visualmap_opts=opts.VisualMapOpts( is_show=True, ), tooltip_opts=opts.TooltipOpts(is_show=True), datazoom_opts=opts.DataZoomOpts(is_show=True) #图形可滑动 ) ) bar.render("pyecharts基本使用.html") 12345678910111213141516171819202122232425

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饼图

from pyecharts import options as opts from pyecharts.charts import Pie from pyecharts.faker import Faker v = Faker.choose() result = [list(z) for z in zip(v, Faker.values())] # 户型占比：[[三室两厅,200] ,[三室一厅,100]，[两室两厅,88] ] print(result) c = ( Pie() .add( "(๑•̀ㅂ•́)و✧", result, radius=["30%", "75%"], center=["50%", "50%"], rosetype="area", ) .set_global_opts( title_opts=opts.TitleOpts(title="Pie-玫瑰图示例"), tooltip_opts=opts.TooltipOpts(is_show=False) ) .set_series_opts( #label_opts:图形上的默认标注的值 # 饼图 formatter: {a}（系列名称），{b}（数据项名称），{c}（数值）, {d}（百分比） label_opts=opts.LabelOpts(is_show=True,formatter="{a}-{b}-{c}-{d}%") ) .render("饼图绘制.html") ) 12345678910111213141516171819202122232425262728

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实例

import pandas as pd from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.globals import ThemeType #读取csv文件 df = pd.read_csv('D:\project\数据分析\lianjia_data.csv',encoding='utf8') #print(df.head()) temp = df.groupby('area')['unit_price'].mean().reset_index() #print(temp) result=[] for index,value in temp.iterrows(): result.append([value['area'],round(value['unit_price']/10000,1)]) print(result) result2 = [ [ value['area'], round(value['unit_price']/10000,1) ] for index,value in temp.iterrows() ] print(result2) # 地图目标数据：[['汕头市', 96], ['汕尾市', 129], ['揭阳市', 114], ['阳江市', 133], ['肇庆市', 75]] c = ( Map(init_opts=opts.InitOpts(theme=ThemeType.DARK)) # 具体参考 pyecharts.datasets.map_filenames.json 文件 .add("深圳各区均价",result2, "深圳") .set_global_opts( title_opts=opts.TitleOpts(title="深圳各区均价"), visualmap_opts=opts.VisualMapOpts(max_=10) ) .render("11-深圳各区均价.html") ) 123456789101112131415161718192021222324252627282930313233

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import pandas as pd from pyecharts import options as opts from pyecharts.charts import Scatter from pyecharts.globals import ThemeType #读取csv文件 df = pd.read_csv('D:\project\数据分析\lianjia_data.csv',encoding='utf8') c = ( Scatter(init_opts=opts.InitOpts(theme=ThemeType.DARK)) .add_xaxis(df['houseSize']) #面积 .add_yaxis("房价-面积散点图",df['total_price']) #房价 .set_global_opts( title_opts=opts.TitleOpts(title="房价-面积散点图"), visualmap_opts=opts.VisualMapOpts(max_=1000), ) .set_series_opts( label_opts=opts.LabelOpts(is_show=False), #标记点 markpoint_opts=opts.MarkPointOpts( data=[ opts.MarkPointItem(name="最低廉的房子",type_="min"), opts.MarkPointItem(name="最奢华的房子",type_="max") ] ) ) .render("scatter_房价_color.html") ) 12345678910111213141516171819202122232425262728

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