上一篇，我们讲解了pyecharts柱状图的常见类型，能够满足基本的工作学习需求，但是pyecharts的功能远不止于此，下面我将继续给大家介绍柱状图进阶展示形式,让你画出更酷炫的柱状图。

1.数据堆叠柱状图

from pyecharts import options as opts
from pyecharts.charts import Bar
l1=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
l2=[100,200,300,400,500,400,300]
l3=[300,400,500,400,300,200,100]
bar = (
    Bar()
    .add_xaxis(l1)
    .add_yaxis("l2", l2, stack="stack1")
    .add_yaxis("l3", l3, stack="stack1")
    .set_global_opts(title_opts=opts.TitleOpts(title="数据堆叠"))
)
bar.render_notebook()

2.柱状图和折线图合并

import pyecharts.options as opts
from pyecharts.charts import Bar, Line


x_data = ["1月", "2月", "3月", "4月", "5月", "6月", "7月", "8月", "9月", "10月", "11月", "12月"]

bar = (
    Bar(init_opts=opts.InitOpts(width="1000px", height="500px"))
    .add_xaxis(xaxis_data=x_data)
    .add_yaxis(
        series_name="降水量",
        yaxis_data=[
            2.6,
            5.9,
            9.0,
            26.4,
            28.7,
            70.7,
            175.6,
            182.2,
            48.7,
            18.8,
            6.0,
            2.3,
        ],
        label_opts=opts.LabelOpts(is_show=False),
    )
    .set_global_opts(
        tooltip_opts=opts.TooltipOpts(
            is_show=True, trigger="axis", axis_pointer_type="cross"
        ),
        xaxis_opts=opts.AxisOpts(
            type_="category",
            axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
        ),
        yaxis_opts=opts.AxisOpts(
            name="水量",
            type_="value",
            min_=0,
            max_=250,
            interval=50,
            axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
            axistick_opts=opts.AxisTickOpts(is_show=True),
            splitline_opts=opts.SplitLineOpts(is_show=True),
        ),
    )
)

line = (
    Line()
    .add_xaxis(xaxis_data=x_data)
    .add_yaxis(
        series_name="降水量",
        yaxis_index=0,
        y_axis=[2.6,5.9,9.0,26.4,28.7,70.7,175.6,182.2,48.7,18.8,6.0,2.3,],
        label_opts=opts.LabelOpts(is_show=False),
    )
)

bar.overlap(line).render_notebook()

yaxis_opts=opts.AxisOpts(）中可以设置纵坐标起止范围和间隔

3.双纵坐标柱状图

如果想把温度和降雨量画在同一个柱状图内，一个纵坐标就不够用了

import pyecharts.options as opts
from pyecharts.charts import Bar, Line
x_data = ["1月", "2月", "3月", "4月", "5月", "6月", "7月", "8月", "9月", "10月", "11月", "12月"]

bar = (
    Bar(init_opts=opts.InitOpts(width="1000px", height="600px"))
    .add_xaxis(xaxis_data=x_data)
    .add_yaxis(
        series_name="蒸发量",
        yaxis_index=0,
        yaxis_data=[
            2.0,
            4.9,
            7.0,
            23.2,
            25.6,
            76.7,
            135.6,
            162.2,
            32.6,
            20.0,
            6.4,
            3.3,
        ],
        label_opts=opts.LabelOpts(is_show=False),
    )
    .add_yaxis(
        series_name="平均温度",
        yaxis_index=1,
        yaxis_data=[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
        label_opts=opts.LabelOpts(is_show=False),
    )
    .extend_axis(
        yaxis=opts.AxisOpts(
            name="温度",
            type_="value",
            min_=0,
            max_=25,
            interval=5,
            axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
        )
    )
    .set_global_opts(
        tooltip_opts=opts.TooltipOpts(
            is_show=True, trigger="axis", axis_pointer_type="cross"
        ),
        xaxis_opts=opts.AxisOpts(
            type_="category",
            axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
        ),
        yaxis_opts=opts.AxisOpts(
            name="水量",
            type_="value",
            min_=0,
            max_=250,
            interval=50,
            axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
            axistick_opts=opts.AxisTickOpts(is_show=True),
            splitline_opts=opts.SplitLineOpts(is_show=True),
        ),
    )
)
bar.render_notebook()

extend_axis:增加了以温度为刻度的纵坐标轴


4.为柱状图添加背景图片

觉得背景太单调？那就来自定义一下背景图片吧

from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
l2=[100,200,300,400,500,400,300]
l3=[300,400,500,400,300,200,100]
bar = (
    Bar(
        init_opts=opts.InitOpts(
            bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"}
        )
    )
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", l2)
    .add_yaxis("商家B", l3)
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="Bar-背景图基本示例",
            subtitle="我是副标题",
            title_textstyle_opts=opts.TextStyleOpts(color="white"),
        )
    )
)
bar.add_js_funcs(
    """
    var img = new Image(); img.src = 'https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1598692410282&di=3a4c3f5770826c30089dd3975357bcd7&imgtype=0&src=http%3A%2F%2Fdimg01.c-ctrip.com%2Fimages%2Ffd%2Ftg%2Fg6%2FM00%2F4A%2F70%2FCggYslbXsPaABfvoAAE_7azp5ow674_R_1024_10000_Q90.jpg';
    """
)
bar.render_notebook()

只需更改img.src中图片url地址即可更换背景

5.为柱状图添加动画

简单的出场方式已经不能满足我的需要了，我需要酷炫一点的

from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker

l1=[100,200,300,400,500,400,300]
l2=[300,400,500,400,300,200,100]
bar = (
    Bar(
        init_opts=opts.InitOpts(
            animation_opts=opts.AnimationOpts(
                animation_delay=1000, animation_easing="bounceIn"
            )
        )
    )
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", l1)
    .add_yaxis("商家B", l2)
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-动画配置基本示例", subtitle="我是副标题"))
)
bar.render_notebook()

animation_delay设置延迟时间

animation_easing设置动画效果

-完-

推荐阅读：

python数据分析——pyecharts柱状图全解（小白必看）

python数据分析之环境搭建——小白也能看懂的Jupyter Notebook安装与使用教程
Python深度分析出高考最难的年份

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