pyecharts的基本使用

官网链接

示例

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(theme=ThemeType.DARK))
    .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])
    .set_global_opts(title_opts=opts.TitleOpts(title="主标题", subtitle="副标题"))
    #设置全局配置
    .set_global_opts(
        title_opts=opts.TitleOpts(title='商家AB销售额对比'),
        legend_opts =opts.LegendOpts(is_show=True),
        toolbox_opts =opts.ToolboxOpts(is_show=True),
        visualmap_opts=opts.VisualMapOpts(is_show=True),
        tooltip_opts =opts.DataZoomOpts(is_show=True)
    )
)
bar.render('haha.html')

疫情数据显示
Bar - Finance_indices_2002
https://gallery.pyecharts.org/#/Bar/finance_indices_2002

• 全局配置项标注的需要掌握
• 系列配置项
• ctrl+f快速搜索

深圳各区均价

import pandas as pd
from pyecharts.globals import  ThemeType
# 1.读取数据
df = pd.read_csv(r'C:\Users\JSJSYS\PycharmProjects\untitled\python实战\lianjia_data.csv')

# 2根据地区分组，求unit_price均值
temp = df.groupby('area')['unit_price'].mean().reset_index()
# dataframe迭代遍历*****
result = []
for index,value in temp.iterrows():
    # index每行数据索引
    # value每行数据对应Series，其索引为列名
    #print(value['area'],value['unit_price'])
    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()
]

from pyecharts import options as opts
from pyecharts.charts import Map
c = (
    Map(init_opts=opts.InitOpts(theme=ThemeType.DARK))
    # 地图类型，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="Map-深圳地图"),
        visualmap_opts=opts.VisualMapOpts(max_=10)
    )
    .render("11-深圳各区均价.html")

房价面积散点图

import  pandas as pd

#1. 读取数据
df = pd.read_csv(r'C:\Users\JSJSYS\PycharmProjects\untitled\python实战\lianjia_data.csv')

#2 散点图
from pyecharts import options as opts
from pyecharts.charts import Scatter
from pyecharts.globals import ThemeType
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（人们的侧重）
    .set_global_opts(
        title_opts=opts.TitleOpts(title="房价-面积散点图"),
        visualmap_opts=opts.VisualMapOpts(max_=150),
    )
    .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_visualmap_color.html")
)