# import sys
# sys.path.append("../")
import pandas as pd
from pyecharts.faker import Faker
from pandasecharts import echart

products = Faker.choose()
dfs = []
for year in (2012, 2013, 2014, 2015):
    dfs.append(
        pd.DataFrame(
            zip(products, Faker.values(), Faker.values(), [year]*len(products)),
            columns=["商品", "商家A", "商家B", "年份"]
        )
    )
df = pd.concat(dfs, axis=0)

df.head()

	商品	商家A	商家B	年份
0	小米	71	63	2012
1	三星	72	85	2012
2	华为	76	65	2012
3	苹果	93	42	2012
4	魅族	122	51	2012

显示2012年商家A的商品销量分布

df.query("年份==2012").echart.pie("商品", "商家A").render_notebook()

label设置

df.query("年份==2012").echart.pie("商品", "商家A", label_opts={"position": "outer"}).render_notebook()

pie样式之半径设置

df.query("年份==2012").echart.pie("商品", "商家A", 
                                 label_opts={"position": "outer"},
                                 radius = ["50%", "70%"],
                                 ).render_notebook()

pie样式之rosetype设置

df.query("年份==2012").echart.pie("商品", "商家A", 
                                 label_show=False,
                                 rosetype = "radius"
                                 ).render_notebook()

legend设置

df.query("年份==2012").echart.pie("商品", "商家A", label_show=False, 
                                 legend_opts={"orient": "vertical", "pos_left": "80%"}
                                 ).render_notebook()

主题设置

df.query("年份==2012").echart.pie("商品", "商家A", theme="light", label_show=False).render_notebook()

所有年份总销量分布

df.echart.pie("商品", "商家A", label_show=False, agg_func="sum").render_notebook()

按照年份显示多张图表

df.echart.pie("商品", "商家A", label_show=False, by="年份").render_notebook()

以时间轴显示多个年份

df.echart.pie("商品", "商家A", 
               timeline="年份",
               label_show=False,
              ).render_notebook()