# 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	衬衫	124	69	2012
1	毛衣	98	45	2012
2	领带	140	45	2012
3	裤子	112	70	2012
4	风衣	142	71	2012

商家A总销量与年份的关系

df.echart.line("年份", "商家A", agg_func="sum", xtype="category", smooth=True).render_notebook()

所有商家总销量与年份的关系

df.echart.line("年份", ["商家A", "商家B"], agg_func="sum", xtype="category").render_notebook()

主题设置

df.echart.line("年份", ["商家A", "商家B"], agg_func="sum", xtype="category", theme="light").render_notebook()

多个Y轴显示

df.echart.line("年份", ["商家A", "商家B"], agg_func="sum", xtype="category", multiple_yaxis=True).render_notebook()

不同商品在所有商家的年销量

df.echart.line("年份", ["商家A", "商家B"], xtype="category", by="商品", multiple_yaxis=True).render_notebook()

将不同商品作为时间轴来显示

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