总共22个完整的pyecharts例子，包含常用的配置方法，每个小例子都包含完整代码，为避免混淆，每个例子都差不多只包含单一配置的代码，更多有趣的源码分享可以在评论区回复。
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1. 柱状图堆叠

不同系列的数据使用相同的stack值会堆叠在一起；

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


def bar_stack():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(Faker.choose())
    # stack值一样的系列会堆叠在一起
    bar.add_yaxis('A', Faker.values(), stack='stack1')
    bar.add_yaxis('B', Faker.values(), stack='stack1')
    bar.add_yaxis('C', Faker.values(), stack='stack2')
    return bar



chart = bar_stack()
chart.render_notebook()

2.关闭坐标轴显示

碰上类目标签过长的时候，可以选择关闭坐标轴，将数据&标签直接显示在图形中

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


def bar_with_axis_off():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(Faker.choose())
    bar.add_yaxis('', Faker.values())
    # 碰上类目标签过长的时候，可以选择关闭坐标轴，直接显示在图形中
    bar.set_series_opts(label_opts=opts.LabelOpts(position='insideLeft', formatter='{b}:{c}'))
    bar.set_global_opts(xaxis_opts=opts.AxisOpts(is_show=False),
                        yaxis_opts=opts.AxisOpts(is_show=False))
    bar.reversal_axis()
    return bar


chart = bar_with_axis_off()
chart.render_notebook()

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3.更改坐标轴数据类型

x轴默认数据类型是使用离散型，在使用散点图的时候可调整为数值型
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from pyecharts.charts import *
from pyecharts import options as opts
import random

x_data = [random.randint(0, 20) for _ in range(100)]
y_data = [random.randint(0, 50) for _ in range(100)]


def scatter_with_value_xaxis():
    scatter = Scatter(init_opts=opts.InitOpts(theme='light',
                                              width='1000px',
                                              height='600px'))
    scatter.add_xaxis(x_data)
    scatter.add_yaxis('', y_data)
    # X轴默认数据类型为离散数据，设置为数值型
    scatter.set_global_opts(xaxis_opts=opts.AxisOpts(type_="value"))
    return scatter

chart = scatter_with_value_xaxis()
chart.render_notebook()

4.双Y轴【直方图&折线图】

实际是Bar和Line两个图表共用同一套坐标体系，将Bar和Line分别指向不同的Y轴
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from pyecharts.charts import *
from pyecharts import options as opts
import random

x_data = ['香蕉', '梨子', '水蜜桃', '核桃', '西瓜', '苹果']
y_data_1 = [random.randint(10, 50) for _ in range(len(x_data))]
y_data_2 = [random.randint(100, 500) for _ in range(len(x_data))]


def bar_line_combine_with_two_axis():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(x_data)
    # 添加一个Y轴
    bar.extend_axis(yaxis=opts.AxisOpts())
    bar.add_yaxis('左边Y轴', y_data_1, yaxis_index=0)

    line = Line(init_opts=opts.InitOpts(theme='light',
                                        width='1000px',
                                        height='600px'))
    line.add_xaxis(x_data)
    # 将line数据通过yaxis_index指向后添加的Y轴
    line.add_yaxis('右边Y轴', y_data_2, yaxis_index=1)

    bar.overlap(line)
    return bar


chart = bar_line_combine_with_two_axis()
chart.render_notebook()

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5.直方图——双Y轴

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from pyecharts.charts import *
from pyecharts import options as opts
import random

x_data = ['香蕉', '梨子', '水蜜桃', '核桃', '西瓜', '苹果']
y_data_1 = [random.randint(10, 50) for _ in range(len(x_data))]
y_data_2 = [random.randint(100, 500) for _ in range(len(x_data))]


def bar_with_multiple_axis():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(x_data)
    # 添加一个Y轴
    bar.extend_axis(yaxis=opts.AxisOpts())
    # 分别指定使用的Y轴
    bar.add_yaxis('左边Y轴', y_data_1, yaxis_index=0)
    bar.add_yaxis('右边Y轴', y_data_2, yaxis_index=1)
    return bar


chart = bar_with_multiple_axis()
chart.render_notebook()

6.折线图——双X轴

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from pyecharts.charts import *
from pyecharts import options as opts
import random

x_data_1 = ["2020/10/{}".format(i + 1) for i in range(30)]
x_data_2 = ["2019/10/{}".format(i + 1) for i in range(30)]
y_data_1 = [random.randint(10, 50) for _ in range(30)]
y_data_2 = [random.randint(20, 60) for _ in range(30)]


def line_with_two_xaxis():
    line = Line(init_opts=opts.InitOpts(theme='light',
                                        width='1000px',
                                        height='600px'))
    line.add_xaxis(x_data_1)
    # 添加一个x轴
    line.extend_axis(xaxis_data=x_data_2, xaxis=opts.AxisOpts())
    line.add_yaxis('下面X轴', y_data_1)
    line.add_yaxis('上面X轴', y_data_2)
    return line


chart = line_with_two_xaxis()
chart.render_notebook()

7.缩略轴——inside组件

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from pyecharts.charts import *
from pyecharts import options as opts
import random

x_data = ["2020/10/{}".format(i + 1) for i in range(30)]

# 随机生成点数据
y_data = [random.randint(10, 20) for i in range(len(x_data))]


def bar_datazoom_inside():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(x_data)
    bar.add_yaxis('', y_data)
    bar.set_global_opts(datazoom_opts=opts.DataZoomOpts(type_='inside',
                                                        range_start=50,   # 设置起止位置，50%-100%
                                                        range_end=100))
    return bar


chart = bar_datazoom_inside()
chart.render_notebook()

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8.缩略轴——slider组件

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from pyecharts.charts import *
from pyecharts import options as opts
import random

x_data = ["2020/10/{}".format(i + 1) for i in range(30)]

# 随机生成点数据
y_data = [random.randint(10, 20) for i in range(len(x_data))]


def bar_with_datazoom_slider():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(x_data)
    bar.add_yaxis('', y_data)
    bar.set_global_opts(datazoom_opts=opts.DataZoomOpts(range_start=50,   # 设置起止位置，50%-100%
                                                        range_end=100))
    return bar


chart = bar_with_datazoom_slider()
chart.render_notebook()

9.XY轴翻转

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


def bar_reverse_axis():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(Faker.choose())
    bar.add_yaxis('A', Faker.values())
    bar.add_yaxis('B', Faker.values())
    # x,y轴翻转
    bar.reversal_axis()
    return bar


chart = bar_reverse_axis()
chart.render_notebook()

10.设置动画效果

在图表加载前会有一段动画效果
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from pyecharts.charts import *
from pyecharts import options as opts
from pyecharts.faker import Faker

"""
更多动画效果可参见：https://echarts.apache.org/examples/zh/editor.html?c=line-easing
"""


def bar_with_animation():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px',
                                      animation_opts=opts.AnimationOpts(animation_delay=1000,   # 动画延时
                                                                        animation_easing='bounceIn')
                                      )
              )
    bar.add_xaxis(Faker.choose())
    bar.add_yaxis('A', Faker.values())
    bar.add_yaxis('B', Faker.values())
    return bar


chart = bar_with_animation()
chart.render_notebook()

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11.直方图带视觉组件

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


def bar_with_visualmap_color():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(Faker.choose())
    bar.add_yaxis('A', Faker.values())
    bar.add_yaxis('B', Faker.values())
    # 设置视觉组件，默认通过颜色映射数据，数值范围为0-100，可通过min_和max_进行自定义
    bar.set_global_opts(visualmap_opts=opts.VisualMapOpts())
    return bar


chart = bar_with_visualmap_color()
chart.render_notebook()

12.设置渐变色(线性渐变)

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from pyecharts.charts import *
from pyecharts import options as opts
from pyecharts.faker import Faker
from pyecharts.commons.utils import JsCode

color_js = """
            new echarts.graphic.LinearGradient(
                                0, 
                                1, 
                                0, 
                                0,
                                [{offset: 0, color: '#008B8B'}, 
                                 {offset: 1, color: '#FF6347'}], 
                                false)
           """


def bar_with_linear_gradient_color():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(Faker.choose())
    bar.add_yaxis('', Faker.values(),
                  # 使用JsCode执行渐变色代码
                  itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_js)))

    return bar


chart = bar_with_linear_gradient_color()
chart.render_notebook()

13.设置分割线

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


def bar_with_custom_splitline():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(Faker.choose())
    bar.add_yaxis('A', Faker.values())
    bar.add_yaxis('B', Faker.values())
    # 设置分割线
    bar.set_global_opts(yaxis_opts=opts.AxisOpts(
        splitline_opts=opts.SplitLineOpts(is_show=True,
                                          linestyle_opts=opts.LineStyleOpts(type_='dotted'))))
    return bar


chart = bar_with_custom_splitline()
chart.render_notebook()

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14.通过字典配置

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


def bar_with_dict_config():
    bar = Bar(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    bar.add_xaxis(Faker.choose())
    bar.add_yaxis('', Faker.values())
    # 通过字典配置itemstyle
    bar.set_series_opts(itemstyle_opts=dict(color='#008B8B', opacity=0.8))
    return bar


chart = bar_with_dict_config()
chart.render_notebook()

15.GEO 带涟漪效果散点图

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from pyecharts.charts import *
from pyecharts import options as opts


def geo_effect_scatter():
    geo = Geo(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))

    geo.add_schema(maptype="china")

    geo.add("",
            [("广州", 150), ("北京", 70), ("长沙", 64), ("上海", 74),  ("厦门", 63)],
            # 涟漪效果散点图
            type_='effectScatter')

    return geo


chart = geo_effect_scatter()
chart.render_notebook()

16.GEO热力图

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from pyecharts.charts import *
from pyecharts import options as opts


def geo_heatmap():
    geo = Geo(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))

    geo.add_schema(maptype="china")

    geo.add("",
            [("广州", 150), ("北京", 70), ("深圳", 64), ("上海", 74),  ("杭州", 63)],
            type_='heatmap')
    # 热点图必须配置visualmap_opts
    geo.set_global_opts(visualmap_opts=opts.VisualMapOpts())
    return geo


chart = geo_heatmap()
chart.render_notebook()

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17.Map带视觉组件

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from pyecharts.charts import *
from pyecharts import options as opts

data = [('广东', 10430),
        ('山东', 9579),
        ('河南', 9402),
        ('四川', 8041),
        ('江苏', 7866),
        ('河北', 7185),
        ('湖南', 6568),
        ('安徽', 5950),
        ('湖北', 5724),
        ('浙江', 5442),
        ('广西', 4603),
        ('云南', 4597),
        ('江西', 4457),
        ('辽宁', 4375),
        ('黑龙江', 3831),
        ('陕西', 3733),
        ('山西', 3571),
        ('福建', 3552),
        ('贵州', 3477),
        ('重庆', 2884),
        ('吉林', 2746),
        ('甘肃省', 2557),
        ('内蒙古', 2471),
        ('台湾', 2316),
        ('上海', 2301),
        ('新疆', 2181),
        ('北京', 1961),
        ('天津', 1294),
        ('海南', 867),
        ('香港', 710),
        ('宁夏', 630),
        ('青海', 562),
        ('西藏', 300),
        ('澳门', 55)]


def map_with_viusalmap():
    map_chart = Map(init_opts=opts.InitOpts(theme='light',
                                            width='1000px',
                                            height='600px'))
    map_chart.add('人口（万人）',
                  data_pair=data,
                  maptype='china',
                  # 关闭symbol的显示
                  is_map_symbol_show=False)

    map_chart.set_global_opts(visualmap_opts=opts.VisualMapOpts(
        max_=10430,  # visualmap默认映射数据范围是【0，100】，需调整
        is_piecewise=True,
        range_color=["#CCD3D9", "#E6B6C2", "#D4587A", "#DC364C"]
    ))
    return map_chart


chart = map_with_viusalmap()
chart.render_notebook()

18.面积图

实现方式就是在折线图先填充区域
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from pyecharts.charts import *
from pyecharts import options as opts
import random

x_data = ["2020/10/{}".format(i + 1) for i in range(30)]

# 随机生成点数据
y_data = [i + random.randint(10, 20) for i in range(len(x_data))]


def line_with_area():
    line = Line(init_opts=opts.InitOpts(theme='light',
                                        width='1000px',
                                        height='600px'))
    line.add_xaxis(x_data)
    line.add_yaxis('', y_data)
    # opacity 默认为0，即透明
    line.set_series_opts(areastyle_opts=opts.AreaStyleOpts(opacity=0.5))

    return line


chart = line_with_area()
chart.render_notebook()

19.堆叠面积图

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


def line_stack_area():
    line = Line(init_opts=opts.InitOpts(theme='light',
                                        width='1000px',
                                        height='600px'))
    line.add_xaxis(Faker.choose())
    line.add_yaxis('A',
                   Faker.values(),
                   stack='stack')
    line.add_yaxis('B',
                   Faker.values(),
                   stack='stack')
    line.add_yaxis('C',
                   Faker.values(),
                   stack='stack')
    # opacity 默认为0，即透明
    line.set_series_opts(areastyle_opts=opts.AreaStyleOpts(opacity=0.5))

    return line


chart = line_stack_area()
chart.render_notebook()

20.饼图自定义图形半径范围

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


def pie_custom_radius():
    pie = Pie(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    pie.add("",
            [list(z) for z in zip(Faker.choose(), Faker.values())],
            # 设置半径范围，0%-100%
            radius=["40%", "75%"])

    return pie


chart = pie_custom_radius()
chart.render_notebook()

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21.饼图自定义数据标签

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


def pie_with_custom_label():
    pie = Pie(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='600px'))
    pie.add("", [list(z) for z in zip(Faker.choose(), Faker.values())])
    pie.set_series_opts(
        # 自定义数据标签
        label_opts=opts.LabelOpts(position='top',
                                  color='red',
                                  font_family='Arial',
                                  font_size=12,
                                  font_style='italic',
                                  interval=1,
                                  formatter='{b}:{d}%'
                                  )
    )

    return pie


chart = pie_with_custom_label()
chart.render_notebook()

22.多饼图

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


def pie_multiple():
    pie = Pie(init_opts=opts.InitOpts(theme='light',
                                      width='1000px',
                                      height='800px'))
    pie.add("",
            [list(z) for z in zip(Faker.choose(), Faker.values())],
            radius=["20%", "50%"],
            center=["25%", "50%"])
    # 添加多个饼图
    pie.add("",
            [list(z) for z in zip(Faker.choose(), Faker.values())],
            radius=["20%", "50%"],
            center=["75%", "50%"])

    return pie


chart = pie_multiple()
chart.render_notebook()

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