import pandas as pd
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType
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	河马	111	62	2012
1	蟒蛇	128	89	2012
2	老虎	70	47	2012
3	大象	143	148	2012
4	兔子	38	66	2012

显示2012年商品在商家A的销量

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

坐标值反转

df.query("年份==2012").echart.bar("商品", "商家A", reverse_axis=True).render_notebook()

显示2012年商品在所有商家的销量

df.query("年份==2012").echart.bar("商品", ["商家A", "商家B"]).render_notebook()

堆叠显示

df.query("年份==2012").echart.bar("商品", ["商家A", "商家B"], stack_view=True).render_notebook()

设置x轴，y轴名称以及标题和副标题

df.query("年份==2012").echart.bar("商品", ["商家A", "商家B"], 
                                  xaxis_name="Product", 
                                  yaxis_name="sales", 
                                  title="Product sales count in 2012",
                                  subtitle="The subtitle").render_notebook()

label设置

df.query("年份==2012").echart.bar("商品", ["商家A", "商家B"], 
                                  label_show=True).render_notebook()

df.query("年份==2012").echart.bar("商品", ["商家A", "商家B"], 
                                  label_show=True,
                                  label_opts={"formatter": "{b}:{c}"}).render_notebook()

df.query("年份==2012").echart.bar("商品", ["商家A", "商家B"], 
                                  label_show=True,
                                  reverse_axis=True,
                                  label_opts={"position": "right"}).render_notebook()

legend设置

df.query("年份==2012").echart.bar("商品", ["商家A", "商家B"], 
                                  legend_opts={"is_show": True, "pos_left": "left"}
                                  ).render_notebook()

主题设置

df.query("年份==2012").echart.bar("商品", ["商家A", "商家B"], 
                                  theme=ThemeType.LIGHT,
                                  ).render_notebook()

所有年份的总销量/平均销量

df.echart.bar("商品", ["商家A", "商家B"], 
               agg_func="sum",
              ).render_notebook()

df.echart.bar("商品", ["商家A", "商家B"], 
               agg_func="mean",
              ).render_notebook()

按照年份显示多张图表

df.echart.bar("商品", ["商家A", "商家B"], 
               by="年份",
              ).render_notebook()

以时间轴显示多个年份

df.echart.bar("商品", ["商家A", "商家B"], 
               timeline="年份",
              ).render_notebook()

时间轴动态排序

df.echart.bar("商品", "商家A", 
               timeline="年份",
               reverse_axis=True,
               sort="商家A",
               label_show=True,
               timeline_opts={"is_auto_play": True}
              ).render_notebook()