Python-数据分析可视化实例图

一：3D纹理图

运行效果图：

Python代码：

import math
from typing import Union

import pyecharts.options as opts
from pyecharts.charts import Surface3D


def float_range(start: int, end: int, step: Union[int, float], round_number: int = 2):
    """
    浮点数 range
    :param start: 起始值
    :param end: 结束值
    :param step: 步长
    :param round_number: 精度
    :return: 返回一个 list
    """
    temp = []
    while True:
        if start < end:
            temp.append(round(start, round_number))
            start += step
        else:
            break
    return temp


def surface3d_data():
    for t0 in float_range(-3, 3, 0.05):
        y = t0
        for t1 in float_range(-3, 3, 0.05):
            x = t1
            z = math.sin(x ** 2 + y ** 2) * x / 3.14
            yield [x, y, z]


(
    Surface3D(init_opts=opts.InitOpts(width="1600px", height="800px"))
    .add(
        series_name="",
        shading="color",
        data=list(surface3d_data()),
        xaxis3d_opts=opts.Axis3DOpts(type_="value"),
        yaxis3d_opts=opts.Axis3DOpts(type_="value"),
        grid3d_opts=opts.Grid3DOpts(width=100, height=40, depth=100),
    )
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(
            dimension=2,
            max_=1,
            min_=-1,
            range_color=[
                "#313695",
                "#4575b4",
                "#74add1",
                "#abd9e9",
                "#e0f3f8",
                "#ffffbf",
                "#fee090",
                "#fdae61",
                "#f46d43",
                "#d73027",
                "#a50026",
            ],
        )
    )
    .render("surface_wave.html")
)

二：3D散点图

运行效果图：

Python代码：

import asyncio
from aiohttp import TCPConnector, ClientSession

import pyecharts.options as opts
from pyecharts.charts import Scatter3D

async def get_json_data(url: str) -> dict:
    async with ClientSession(connector=TCPConnector(ssl=False)) as session:
        async with session.get(url=url) as response:
            return await response.json()

# 获取官方的数据
data = asyncio.run(
    get_json_data(
        url="https://echarts.apache.org/examples/data/asset/data/nutrients.json"
    )
)

# 列名映射
field_indices = {
    "calcium": 3,
    "calories": 12,
    "carbohydrate": 8,
    "fat": 10,
    "fiber": 5,
    "group": 1,
    "id": 16,
    "monounsat": 14,
    "name": 0,
    "polyunsat": 15,
    "potassium": 7,
    "protein": 2,
    "saturated": 13,
    "sodium": 4,
    "sugars": 9,
    "vitaminc": 6,
    "water": 11,
}

# 配置 config
config_xAxis3D = "protein"
config_yAxis3D = "fiber"
config_zAxis3D = "sodium"
config_color = "fiber"
config_symbolSize = "vitaminc"

# 构造数据
data = [
    [
        item[field_indices[config_xAxis3D]],
        item[field_indices[config_yAxis3D]],
        item[field_indices[config_zAxis3D]],
        item[field_indices[config_color]],
        item[field_indices[config_symbolSize]],
        index,
    ]
    for index, item in enumerate(data)
]

(
    Scatter3D(
        init_opts=opts.InitOpts(width="1440px", height="720px")
    )  # bg_color="black"
    .add(
        series_name="",
        data=data,
        xaxis3d_opts=opts.Axis3DOpts(
            name=config_xAxis3D,
            type_="value",
            # textstyle_opts=opts.TextStyleOpts(color="#fff"),
        ),
        yaxis3d_opts=opts.Axis3DOpts(
            name=config_yAxis3D,
            type_="value",
            # textstyle_opts=opts.TextStyleOpts(color="#fff"),
        ),
        zaxis3d_opts=opts.Axis3DOpts(
            name=config_zAxis3D,
            type_="value",
            # textstyle_opts=opts.TextStyleOpts(color="#fff"),
        ),
        grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),
    )
    .set_global_opts(
        visualmap_opts=[
            opts.VisualMapOpts(
                type_="color",
                is_calculable=True,
                dimension=3,
                pos_top="10",
                max_=79 / 2,
                range_color=[
                    "#1710c0",
                    "#0b9df0",
                    "#00fea8",
                    "#00ff0d",
                    "#f5f811",
                    "#f09a09",
                    "#fe0300",
                ],
            ),
            opts.VisualMapOpts(
                type_="size",
                is_calculable=True,
                dimension=4,
                pos_bottom="10",
                max_=2.4 / 2,
                range_size=[10, 40],
            ),
        ]
    )
    .render("scatter3d.html")
)

三：3D折线图

运行效果图：

Python代码：

import math

import pyecharts.options as opts
from pyecharts.charts import Line3D

week_en = "Saturday Friday Thursday Wednesday Tuesday Monday Sunday".split()
clock = (
    "12a 1a 2a 3a 4a 5a 6a 7a 8a 9a 10a 11a 12p "
    "1p 2p 3p 4p 5p 6p 7p 8p 9p 10p 11p".split()
)

data = []
for t in range(0, 25000):
    _t = t / 1000
    x = (1 + 0.25 * math.cos(75 * _t)) * math.cos(_t)
    y = (1 + 0.25 * math.cos(75 * _t)) * math.sin(_t)
    z = _t + 2.0 * math.sin(75 * _t)
    data.append([x, y, z])

(
    Line3D()
    .add(
        "",
        data,
        xaxis3d_opts=opts.Axis3DOpts(data=clock, type_="value"),
        yaxis3d_opts=opts.Axis3DOpts(data=week_en, type_="value"),
        grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),
    )
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(
            dimension=2,
            max_=30,
            min_=0,
            range_color=[
                "#313695",
                "#4575b4",
                "#74add1",
                "#abd9e9",
                "#e0f3f8",
                "#ffffbf",
                "#fee090",
                "#fdae61",
                "#f46d43",
                "#d73027",
                "#a50026",
            ],
        )
    )
    .render("line3d_rectangular_projection.html")
)

四：3D柱状图

（一）运行效果图：

Python代码：

import random

from pyecharts import options as opts
from pyecharts.charts import Bar3D
from pyecharts.faker import Faker


data = [(i, j, random.randint(0, 12)) for i in range(6) for j in range(24)]
c = (
    Bar3D()
    .add(
        "",
        [[d[1], d[0], d[2]] for d in data],
        xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="category"),
        yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="category"),
        zaxis3d_opts=opts.Axis3DOpts(type_="value"),
    )
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(max_=20),
        title_opts=opts.TitleOpts(title="3D-基本示例"),
    )
    .render("bar3d_base.html")
)

（二）运行效果图：

Python代码：

import pyecharts.options as opts
from pyecharts.charts import Bar3D

hours = [
    "12a",
    "1a",
    "2a",
    "3a",
    "4a",
    "5a",
    "6a",
    "7a",
    "8a",
    "9a",
    "10a",
    "11a",
    "12p",
    "1p",
    "2p",
    "3p",
    "4p",
    "5p",
    "6p",
    "7p",
    "8p",
    "9p",
    "10p",
    "11p",
]
days = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]

data = [
    [0, 0, 5],
    [0, 1, 1],
    [0, 2, 0],
    [0, 3, 0],
    [0, 4, 0],
    [0, 5, 0],
    [0, 6, 0],
    [0, 7, 0],
    [0, 8, 0],
    [0, 9, 0],
    [0, 10, 0],
    [0, 11, 2],
    [0, 12, 4],
    [0, 13, 1],
    [0, 14, 1],
    [0, 15, 3],
    [0, 16, 4],
    [0, 17, 6],
    [0, 18, 4],
    [0, 19, 4],
    [0, 20, 3],
    [0, 21, 3],
    [0, 22, 2],
    [0, 23, 5],
    [1, 0, 7],
    [1, 1, 0],
    [1, 2, 0],
    [1, 3, 0],
    [1, 4, 0],
    [1, 5, 0],
    [1, 6, 0],
    [1, 7, 0],
    [1, 8, 0],
    [1, 9, 0],
    [1, 10, 5],
    [1, 11, 2],
    [1, 12, 2],
    [1, 13, 6],
    [1, 14, 9],
    [1, 15, 11],
    [1, 16, 6],
    [1, 17, 7],
    [1, 18, 8],
    [1, 19, 12],
    [1, 20, 5],
    [1, 21, 5],
    [1, 22, 7],
    [1, 23, 2],
    [2, 0, 1],
    [2, 1, 1],
    [2, 2, 0],
    [2, 3, 0],
    [2, 4, 0],
    [2, 5, 0],
    [2, 6, 0],
    [2, 7, 0],
    [2, 8, 0],
    [2, 9, 0],
    [2, 10, 3],
    [2, 11, 2],
    [2, 12, 1],
    [2, 13, 9],
    [2, 14, 8],
    [2, 15, 10],
    [2, 16, 6],
    [2, 17, 5],
    [2, 18, 5],
    [2, 19, 5],
    [2, 20, 7],
    [2, 21, 4],
    [2, 22, 2],
    [2, 23, 4],
    [3, 0, 7],
    [3, 1, 3],
    [3, 2, 0],
    [3, 3, 0],
    [3, 4, 0],
    [3, 5, 0],
    [3, 6, 0],
    [3, 7, 0],
    [3, 8, 1],
    [3, 9, 0],
    [3, 10, 5],
    [3, 11, 4],
    [3, 12, 7],
    [3, 13, 14],
    [3, 14, 13],
    [3, 15, 12],
    [3, 16, 9],
    [3, 17, 5],
    [3, 18, 5],
    [3, 19, 10],
    [3, 20, 6],
    [3, 21, 4],
    [3, 22, 4],
    [3, 23, 1],
    [4, 0, 1],
    [4, 1, 3],
    [4, 2, 0],
    [4, 3, 0],
    [4, 4, 0],
    [4, 5, 1],
    [4, 6, 0],
    [4, 7, 0],
    [4, 8, 0],
    [4, 9, 2],
    [4, 10, 4],
    [4, 11, 4],
    [4, 12, 2],
    [4, 13, 4],
    [4, 14, 4],
    [4, 15, 14],
    [4, 16, 12],
    [4, 17, 1],
    [4, 18, 8],
    [4, 19, 5],
    [4, 20, 3],
    [4, 21, 7],
    [4, 22, 3],
    [4, 23, 0],
    [5, 0, 2],
    [5, 1, 1],
    [5, 2, 0],
    [5, 3, 3],
    [5, 4, 0],
    [5, 5, 0],
    [5, 6, 0],
    [5, 7, 0],
    [5, 8, 2],
    [5, 9, 0],
    [5, 10, 4],
    [5, 11, 1],
    [5, 12, 5],
    [5, 13, 10],
    [5, 14, 5],
    [5, 15, 7],
    [5, 16, 11],
    [5, 17, 6],
    [5, 18, 0],
    [5, 19, 5],
    [5, 20, 3],
    [5, 21, 4],
    [5, 22, 2],
    [5, 23, 0],
    [6, 0, 1],
    [6, 1, 0],
    [6, 2, 0],
    [6, 3, 0],
    [6, 4, 0],
    [6, 5, 0],
    [6, 6, 0],
    [6, 7, 0],
    [6, 8, 0],
    [6, 9, 0],
    [6, 10, 1],
    [6, 11, 0],
    [6, 12, 2],
    [6, 13, 1],
    [6, 14, 3],
    [6, 15, 4],
    [6, 16, 0],
    [6, 17, 0],
    [6, 18, 0],
    [6, 19, 0],
    [6, 20, 1],
    [6, 21, 2],
    [6, 22, 2],
    [6, 23, 6],
]
data = [[d[1], d[0], d[2]] for d in data]


(
    Bar3D(init_opts=opts.InitOpts(width="1600px", height="800px"))
    .add(
        series_name="",
        data=data,
        xaxis3d_opts=opts.Axis3DOpts(type_="category", data=hours),
        yaxis3d_opts=opts.Axis3DOpts(type_="category", data=days),
        zaxis3d_opts=opts.Axis3DOpts(type_="value"),
    )
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(
            max_=20,
            range_color=[
                "#313695",
                "#4575b4",
                "#74add1",
                "#abd9e9",
                "#e0f3f8",
                "#ffffbf",
                "#fee090",
                "#fdae61",
                "#f46d43",
                "#d73027",
                "#a50026",
            ],
        )
    )
    .render("bar3d_punch_card.html")
)

（三）运行效果图：

Python代码：

import random

from pyecharts import options as opts
from pyecharts.charts import Bar3D

x_data = y_data = list(range(10))


def generate_data():
    data = []
    for j in range(10):
        for k in range(10):
            value = random.randint(0, 9)
            data.append([j, k, value * 2 + 4])
    return data


bar3d = Bar3D()
for _ in range(10):
    bar3d.add(
        "",
        generate_data(),
        shading="lambert",
        xaxis3d_opts=opts.Axis3DOpts(data=x_data, type_="value"),
        yaxis3d_opts=opts.Axis3DOpts(data=y_data, type_="value"),
        zaxis3d_opts=opts.Axis3DOpts(type_="value"),
    )
bar3d.set_global_opts(title_opts=opts.TitleOpts("3D-堆叠柱状图示例"))
bar3d.set_series_opts(**{"stack": "stack"})
bar3d.render("bar3d_stack.html")

五：饼状图

（一）运行效果图：

Python代码：

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

v = Faker.choose()
c = (
    Pie()
    .add(
        "",
        [list(z) for z in zip(v, Faker.values())],
        radius=["30%", "75%"],
        center=["25%", "50%"],
        rosetype="radius",
        label_opts=opts.LabelOpts(is_show=False),
    )
    .add(
        "",
        [list(z) for z in zip(v, Faker.values())],
        radius=["30%", "75%"],
        center=["75%", "50%"],
        rosetype="area",
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="Pie-玫瑰图示例"))
    .render("pie_rosetype.html")
)

（二）运行效果图：

Python代码：

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

c = (
    Pie()
    .add(
        "",
        [list(z) for z in zip(Faker.choose(), Faker.values())],
        radius=["40%", "55%"],
        label_opts=opts.LabelOpts(
            position="outside",
            formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c}  {per|{d}%}  ",
            background_color="#eee",
            border_color="#aaa",
            border_width=1,
            border_radius=4,
            rich={
                "a": {"color": "#999", "lineHeight": 22, "align": "center"},
                "abg": {
                    "backgroundColor": "#e3e3e3",
                    "width": "100%",
                    "align": "right",
                    "height": 22,
                    "borderRadius": [4, 4, 0, 0],
                },
                "hr": {
                    "borderColor": "#aaa",
                    "width": "100%",
                    "borderWidth": 0.5,
                    "height": 0,
                },
                "b": {"fontSize": 16, "lineHeight": 33},
                "per": {
                    "color": "#eee",
                    "backgroundColor": "#334455",
                    "padding": [2, 4],
                    "borderRadius": 2,
                },
            },
        ),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="Pie-富文本示例"))
    .render("pie_rich_label.html")
)

（三）运行效果图：

Python代码：

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

inner_x_data = ["直达", "营销广告", "搜索引擎"]
inner_y_data = [335, 679, 1548]
inner_data_pair = [list(z) for z in zip(inner_x_data, inner_y_data)]

outer_x_data = ["直达", "营销广告", "搜索引擎", "邮件营销", "联盟广告", "视频广告", "百度", "谷歌", "必应", "其他"]
outer_y_data = [335, 310, 234, 135, 1048, 251, 147, 102]
outer_data_pair = [list(z) for z in zip(outer_x_data, outer_y_data)]

(
    Pie(init_opts=opts.InitOpts(width="1600px", height="800px"))
    .add(
        series_name="访问来源",
        data_pair=inner_data_pair,
        radius=[0, "30%"],
        label_opts=opts.LabelOpts(position="inner"),
    )
    .add(
        series_name="访问来源",
        radius=["40%", "55%"],
        data_pair=outer_data_pair,
        label_opts=opts.LabelOpts(
            position="outside",
            formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c}  {per|{d}%}  ",
            background_color="#eee",
            border_color="#aaa",
            border_width=1,
            border_radius=4,
            rich={
                "a": {"color": "#999", "lineHeight": 22, "align": "center"},
                "abg": {
                    "backgroundColor": "#e3e3e3",
                    "width": "100%",
                    "align": "right",
                    "height": 22,
                    "borderRadius": [4, 4, 0, 0],
                },
                "hr": {
                    "borderColor": "#aaa",
                    "width": "100%",
                    "borderWidth": 0.5,
                    "height": 0,
                },
                "b": {"fontSize": 16, "lineHeight": 33},
                "per": {
                    "color": "#eee",
                    "backgroundColor": "#334455",
                    "padding": [2, 4],
                    "borderRadius": 2,
                },
            },
        ),
    )
    .set_global_opts(legend_opts=opts.LegendOpts(pos_left="left", orient="vertical"))
    .set_series_opts(
        tooltip_opts=opts.TooltipOpts(
            trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
        )
    )
    .render("nested_pies.html")
)

（四）运行效果图：

Python代码：

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.commons.utils import JsCode


fn = """
    function(params) {
        if(params.name == '其他')
            return '\\n\\n\\n' + params.name + ' : ' + params.value + '%';
        return params.name + ' : ' + params.value + '%';
    }
    """


def new_label_opts():
    return opts.LabelOpts(formatter=JsCode(fn), position="center")


c = (
    Pie()
    .add(
        "",
        [list(z) for z in zip(["剧情", "其他"], [25, 75])],
        center=["20%", "30%"],
        radius=[60, 80],
        label_opts=new_label_opts(),
    )
    .add(
        "",
        [list(z) for z in zip(["奇幻", "其他"], [24, 76])],
        center=["55%", "30%"],
        radius=[60, 80],
        label_opts=new_label_opts(),
    )
    .add(
        "",
        [list(z) for z in zip(["爱情", "其他"], [14, 86])],
        center=["20%", "70%"],
        radius=[60, 80],
        label_opts=new_label_opts(),
    )
    .add(
        "",
        [list(z) for z in zip(["惊悚", "其他"], [11, 89])],
        center=["55%", "70%"],
        radius=[60, 80],
        label_opts=new_label_opts(),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Pie-多饼图基本示例"),
        legend_opts=opts.LegendOpts(
            type_="scroll", pos_top="20%", pos_left="80%", orient="vertical"
        ),
    )
    .render("mutiple_pie.html")
)

（5）运行效果图：

Python代码：

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

c = (
    Pie()
    .add(
        "",
        [list(z) for z in zip(Faker.choose(), Faker.values())],
        radius=["40%", "75%"],
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Pie-Radius"),
        legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"),
    )
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    .render("pie_radius.html")
)

六：词云图

（一）运行效果图：

Python代码：

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



data = [
    ("生活资源", "999"),
    ("供热管理", "888"),
    ("供气质量", "777"),
    ("生活用水管理", "688"),
    ("一次供水问题", "588"),
    ("交通运输", "516"),
    ("城市交通", "515"),
    ("环境保护", "483"),
    ("房地产管理", "462"),
    ("城乡建设", "449"),
    ("社会保障与福利", "429"),
    ("社会保障", "407"),
    ("文体与教育管理", "406"),
    ("公共安全", "406"),
    ("公交运输管理", "386"),
    ("出租车运营管理", "385"),
    ("供热管理", "375"),
    ("市容环卫", "355"),
    ("自然资源管理", "355"),
    ("粉尘污染", "335"),
    ("噪声污染", "324"),
    ("土地资源管理", "304"),
    ("物业服务与管理", "304"),
    ("医疗卫生", "284"),
    ("粉煤灰污染", "284"),
    ("占道", "284"),
    ("供热发展", "254"),
    ("农村土地规划管理", "254"),
    ("生活噪音", "253"),
    ("供热单位影响", "253"),
    ("城市供电", "223"),
    ("房屋质量与安全", "223"),
    ("大气污染", "223"),
    ("房屋安全", "223"),
    ("文化活动", "223"),
    ("拆迁管理", "223"),
    ("公共设施", "223"),
    ("供气质量", "223"),
    ("供电管理", "223"),
    ("燃气管理", "152"),
    ("教育管理", "152"),
    ("医疗纠纷", "152"),
    ("执法监督", "152"),
    ("设备安全", "152"),
    ("政务建设", "152"),
    ("县区、开发区", "152"),
    ("宏观经济", "152"),
    ("教育管理", "112"),
    ("社会保障", "112"),
    ("生活用水管理", "112"),
    ("物业服务与管理", "112"),
    ("分类列表", "112"),
    ("农业生产", "112"),
    ("二次供水问题", "112"),
    ("城市公共设施", "92"),
    ("拆迁政策咨询", "92"),
    ("物业服务", "92"),
    ("物业管理", "92"),
    ("社会保障保险管理", "92"),
    ("低保管理", "92"),
    ("文娱市场管理", "72"),
    ("城市交通秩序管理", "72"),
    ("执法争议", "72"),
    ("商业烟尘污染", "72"),
    ("占道堆放", "71"),
    ("地上设施", "71"),
    ("水质", "71"),
    ("无水", "71"),
    ("供热单位影响", "71"),
    ("人行道管理", "71"),
    ("主网原因", "71"),
    ("集中供热", "71"),
    ("客运管理", "71"),
    ("国有公交（大巴）管理", "71"),
    ("工业粉尘污染", "71"),
    ("治安案件", "71"),
    ("压力容器安全", "71"),
    ("身份证管理", "71"),
    ("群众健身", "41"),
    ("工业排放污染", "41"),
    ("破坏森林资源", "41"),
    ("市场收费", "41"),
    ("生产资金", "41"),
    ("生产噪声", "41"),
    ("农村低保", "41"),
    ("劳动争议", "41"),
    ("劳动合同争议", "41"),
    ("劳动报酬与福利", "41"),
    ("医疗事故", "21"),
    ("停供", "21"),
    ("基础教育", "21"),
    ("职业教育", "21"),
    ("物业资质管理", "21"),
    ("拆迁补偿", "21"),
    ("设施维护", "21"),
    ("市场外溢", "11"),
    ("占道经营", "11"),
    ("树木管理", "11"),
    ("农村基础设施", "11"),
    ("无水", "11"),
    ("供气质量", "11"),
    ("停气", "11"),
    ("市政府工作部门（含部门管理机构、直属单位）", "11"),
    ("燃气管理", "11"),
    ("市容环卫", "11"),
    ("新闻传媒", "11"),
    ("人才招聘", "11"),
    ("市场环境", "11"),
    ("行政事业收费", "11"),
    ("食品安全与卫生", "11"),
    ("城市交通", "11"),
    ("房地产开发", "11"),
    ("房屋配套问题", "11"),
    ("物业服务", "11"),
    ("物业管理", "11"),
    ("占道", "11"),
    ("园林绿化", "11"),
    ("户籍管理及身份证", "11"),
    ("公交运输管理", "11"),
    ("公路（水路）交通", "11"),
    ("房屋与图纸不符", "11"),
    ("有线电视", "11"),
    ("社会治安", "11"),
    ("林业资源", "11"),
    ("其他行政事业收费", "11"),
    ("经营性收费", "11"),
    ("食品安全与卫生", "11"),
    ("体育活动", "11"),
    ("有线电视安装及调试维护", "11"),
    ("低保管理", "11"),
    ("劳动争议", "11"),
    ("社会福利及事务", "11"),
    ("一次供水问题", "11"),
]

(
    WordCloud()
    .add(series_name="热点分析", data_pair=data, word_size_range=[6, 66])
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="热点分析", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
        ),
        tooltip_opts=opts.TooltipOpts(is_show=True),
    )
    .render("basic_wordcloud.html")
)

（二）运行效果图：

Python代码：

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

words = [
    ("花鸟市场", 1446),
    ("汽车", 928),
    ("视频", 906),
    ("电视", 825),
    ("Lover Boy 88", 514),
    ("动漫", 486),
    ("音乐", 53),
    ("直播", 163),
    ("广播电台", 86),
    ("戏曲曲艺", 17),
    ("演出票务", 6),
    ("给陌生的你听", 1),
    ("资讯", 1437),
    ("商业财经", 422),
    ("娱乐八卦", 353),
    ("军事", 331),
    ("科技资讯", 313),
    ("社会时政", 307),
    ("时尚", 43),
    ("网络奇闻", 15),
    ("旅游出行", 438),
    ("景点类型", 957),
    ("国内游", 927),
    ("远途出行方式", 908),
    ("酒店", 693),
    ("关注景点", 611),
    ("旅游网站偏好", 512),
    ("出国游", 382),
    ("交通票务", 312),
    ("旅游方式", 187),
    ("旅游主题", 163),
    ("港澳台", 104),
    ("本地周边游", 3),
    ("小卖家", 1331),
    ("全日制学校", 941),
    ("基础教育科目", 585),
    ("考试培训", 473),
    ("语言学习", 358),
    ("留学", 246),
    ("K12课程培训", 207),
    ("艺术培训", 194),
    ("技能培训", 104),
    ("IT培训", 87),
    ("高等教育专业", 63),
    ("家教", 48),
    ("体育培训", 23),
    ("职场培训", 5),
    ("金融财经", 1328),
    ("银行", 765),
    ("股票", 452),
    ("保险", 415),
    ("贷款", 253),
    ("基金", 211),
    ("信用卡", 180),
    ("外汇", 138),
    ("P2P", 116),
    ("贵金属", 98),
    ("债券", 93),
    ("网络理财", 92),
    ("信托", 90),
    ("征信", 76),
    ("期货", 76),
    ("公积金", 40),
    ("银行理财", 36),
    ("银行业务", 30),
    ("典当", 7),
    ("海外置业", 1),
    ("汽车", 1309),
    ("汽车档次", 965),
    ("汽车品牌", 900),
    ("汽车车型", 727),
    ("购车阶段", 461),
    ("二手车", 309),
    ("汽车美容", 260),
    ("新能源汽车", 173),
    ("汽车维修", 155),
    ("租车服务", 136),
    ("车展", 121),
    ("违章查询", 76),
    ("汽车改装", 62),
    ("汽车用品", 37),
    ("路况查询", 32),
    ("汽车保险", 28),
    ("陪驾代驾", 4),
    ("网络购物", 1275),
    ("做我的猫", 1088),
    ("只想要你知道", 907),
    ("团购", 837),
    ("比价", 201),
    ("海淘", 195),
    ("移动APP购物", 179),
    ("支付方式", 119),
    ("代购", 43),
    ("体育健身", 1234),
    ("体育赛事项目", 802),
    ("运动项目", 405),
    ("体育类赛事", 337),
    ("健身项目", 199),
    ("健身房健身", 78),
    ("运动健身", 77),
    ("家庭健身", 36),
    ("健身器械", 29),
    ("办公室健身", 3),
    ("商务服务", 1201),
    ("法律咨询", 508),
    ("化工材料", 147),
    ("广告服务", 125),
    ("会计审计", 115),
    ("人员招聘", 101),
    ("印刷打印", 66),
    ("知识产权", 32),
    ("翻译", 22),
    ("安全安保", 9),
    ("公关服务", 8),
    ("商旅服务", 2),
    ("展会服务", 2),
    ("特许经营", 1),
    ("休闲爱好", 1169),
    ("收藏", 412),
    ("摄影", 393),
    ("温泉", 230),
    ("博彩彩票", 211),
    ("美术", 207),
    ("书法", 139),
    ("DIY手工", 75),
    ("舞蹈", 23),
    ("钓鱼", 21),
    ("棋牌桌游", 17),
    ("KTV", 6),
    ("密室", 5),
    ("采摘", 4),
    ("电玩", 1),
    ("真人CS", 1),
    ("轰趴", 1),
    ("家电数码", 1111),
    ("手机", 885),
    ("电脑", 543),
    ("大家电", 321),
    ("家电关注品牌", 253),
    ("网络设备", 162),
    ("摄影器材", 149),
    ("影音设备", 133),
    ("办公数码设备", 113),
    ("生活电器", 67),
    ("厨房电器", 54),
    ("智能设备", 45),
    ("个人护理电器", 22),
    ("服饰鞋包", 1047),
    ("服装", 566),
    ("饰品", 289),
    ("鞋", 184),
    ("箱包", 168),
    ("奢侈品", 137),
    ("母婴亲子", 1041),
    ("孕婴保健", 505),
    ("母婴社区", 299),
    ("早教", 103),
    ("奶粉辅食", 66),
    ("童车童床", 41),
    ("关注品牌", 271),
    ("宝宝玩乐", 30),
    ("母婴护理服务", 25),
    ("纸尿裤湿巾", 16),
    ("妈妈用品", 15),
    ("宝宝起名", 12),
    ("童装童鞋", 9),
    ("胎教", 8),
    ("宝宝安全", 1),
    ("宝宝洗护用品", 1),
    ("软件应用", 1018),
    ("系统工具", 896),
    ("理财购物", 440),
    ("生活实用", 365),
    ("影音图像", 256),
    ("社交通讯", 214),
    ("手机美化", 39),
    ("办公学习", 28),
    ("应用市场", 23),
    ("母婴育儿", 14),
    ("游戏", 946),
    ("手机游戏", 565),
    ("PC游戏", 353),
    ("网页游戏", 254),
    ("游戏机", 188),
    ("模拟辅助", 166),
    ("个护美容", 942),
    ("护肤品", 177),
    ("彩妆", 133),
    ("美发", 80),
    ("香水", 50),
    ("个人护理", 46),
    ("美甲", 26),
    ("SPA美体", 21),
    ("花鸟萌宠", 914),
    ("绿植花卉", 311),
    ("狗", 257),
    ("其他宠物", 131),
    ("水族", 125),
    ("猫", 122),
    ("动物", 81),
    ("鸟", 67),
    ("宠物用品", 41),
    ("宠物服务", 26),
    ("书籍阅读", 913),
    ("网络小说", 483),
    ("关注书籍", 128),
    ("文学", 105),
    ("报刊杂志", 77),
    ("人文社科", 22),
    ("建材家居", 907),
    ("装修建材", 644),
    ("家具", 273),
    ("家居风格", 187),
    ("家居家装关注品牌", 140),
    ("家纺", 107),
    ("厨具", 47),
    ("灯具", 43),
    ("家居饰品", 29),
    ("家居日常用品", 10),
    ("生活服务", 883),
    ("物流配送", 536),
    ("家政服务", 108),
    ("摄影服务", 49),
    ("搬家服务", 38),
    ("物业维修", 37),
    ("婚庆服务", 24),
    ("二手回收", 24),
    ("鲜花配送", 3),
    ("维修服务", 3),
    ("殡葬服务", 1),
    ("求职创业", 874),
    ("创业", 363),
    ("目标职位", 162),
    ("目标行业", 50),
    ("兼职", 21),
    ("期望年薪", 20),
    ("实习", 16),
    ("雇主类型", 10),
    ("星座运势", 789),
    ("星座", 316),
    ("算命", 303),
    ("解梦", 196),
    ("风水", 93),
    ("面相分析", 47),
    ("手相", 32),
    ("公益", 90),
]

c = (
    WordCloud()
    .add(
        "",
        words,
        word_size_range=[20, 100],
        textstyle_opts=opts.TextStyleOpts(font_family="cursive"),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="自定义文字样式"))
    .render("wordcloud_custom_font_style.html")
)

（三）运行效果图：

Python代码：

from pyecharts import options as opts
from pyecharts.charts import WordCloud
from pyecharts.globals import SymbolType

words = [
    ("Sam S Club", 10000),
    ("Macys", 6181),
    ("Amy Schumer", 4386),
    ("Jurassic World", 4055),
    ("Charter Communications", 2467),
    ("Chick Fil A", 2244),
    ("Planet Fitness", 1868),
    ("Pitch Perfect", 1484),
    ("Express", 1112),
    ("Home", 865),
    ("Johnny Depp", 847),
    ("Lena Dunham", 582),
    ("Lewis Hamilton", 555),
    ("KXAN", 550),
    ("Mary Ellen Mark", 462),
    ("Farrah Abraham", 366),
    ("Rita Ora", 360),
    ("Serena Williams", 282),
    ("NCAA baseball tournament", 273),
    ("Point Break", 265),
]
c = (
    WordCloud()
    .add("", words, word_size_range=[20, 100], shape=SymbolType.DIAMOND)
    .set_global_opts(title_opts=opts.TitleOpts(title="WordCloud-shape-diamond"))
    .render("wordcloud_diamond.html")
)

七：雷达图

（一）运行效果图：

Python代码：

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

v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]

(
    Radar(init_opts=opts.InitOpts(width="1280px", height="720px", bg_color="#CCCCCC"))
    .add_schema(
        schema=[
            opts.RadarIndicatorItem(name="销售（sales）", max_=6500),
            opts.RadarIndicatorItem(name="管理（Administration）", max_=16000),
            opts.RadarIndicatorItem(name="信息技术（Information Technology）", max_=30000),
            opts.RadarIndicatorItem(name="客服（Customer Support）", max_=38000),
            opts.RadarIndicatorItem(name="研发（Development）", max_=52000),
            opts.RadarIndicatorItem(name="市场（Marketing）", max_=25000),
        ],
        splitarea_opt=opts.SplitAreaOpts(
            is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
        ),
        textstyle_opts=opts.TextStyleOpts(color="#fff"),
    )
    .add(
        series_name="预算分配（Allocated Budget）",
        data=v1,
        linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
    )
    .add(
        series_name="实际开销（Actual Spending）",
        data=v2,
        linestyle_opts=opts.LineStyleOpts(color="#5CACEE"),
    )
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="基础雷达图"), legend_opts=opts.LegendOpts()
    )
    .render("basic_radar_chart.html")
)

（二）运行效果图：

Python代码：

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

data = [{"value": [4, -4, 2, 3, 0, 1], "name": "预算分配"}]
c_schema = [
    {"name": "销售", "max": 4, "min": -4},
    {"name": "管理", "max": 4, "min": -4},
    {"name": "技术", "max": 4, "min": -4},
    {"name": "客服", "max": 4, "min": -4},
    {"name": "研发", "max": 4, "min": -4},
    {"name": "市场", "max": 4, "min": -4},
]
c = (
    Radar()
    .set_colors(["#4587E7"])
    .add_schema(
        schema=c_schema,
        shape="circle",
        center=["50%", "50%"],
        radius="80%",
        angleaxis_opts=opts.AngleAxisOpts(
            min_=0,
            max_=360,
            is_clockwise=False,
            interval=5,
            axistick_opts=opts.AxisTickOpts(is_show=False),
            axislabel_opts=opts.LabelOpts(is_show=False),
            axisline_opts=opts.AxisLineOpts(is_show=False),
            splitline_opts=opts.SplitLineOpts(is_show=False),
        ),
        radiusaxis_opts=opts.RadiusAxisOpts(
            min_=-4,
            max_=4,
            interval=2,
            splitarea_opts=opts.SplitAreaOpts(
                is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
            ),
        ),
        polar_opts=opts.PolarOpts(),
        splitarea_opt=opts.SplitAreaOpts(is_show=False),
        splitline_opt=opts.SplitLineOpts(is_show=False),
    )
    .add(
        series_name="预算",
        data=data,
        areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
        linestyle_opts=opts.LineStyleOpts(width=1),
    )
    .render("radar_angle_radius_axis.html")
)

八：漏斗图

（一）运行效果图：

Python代码：

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


c = (
    Funnel()
    .add(
        "漏斗图",
        [list(z) for z in zip(Faker.choose(), Faker.values())],
        label_opts=opts.LabelOpts(position="inside"),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="Funnel-Label（inside)"))
    .render("funnel_label_inside.html")
)

（一）运行效果图：

Python代码：

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

c = (
    Funnel()
    .add(
        "漏斗图",
        [list(z) for z in zip(Faker.choose(), Faker.values())],
        sort_="ascending",
        label_opts=opts.LabelOpts(position="inside"),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="Funnel-Sort（ascending）"))
    .render("funnel_sort_ascending.html")
)

九：地理坐标图

（一）运行效果图：

Python代码：

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolType

c = (
    Geo()
    .add_schema(
        maptype="china",
        itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),
    )
    .add(
        "",
        [("广州", 55), ("北京", 66), ("杭州", 77), ("重庆", 88)],
        type_=ChartType.EFFECT_SCATTER,
        color="white",
    )
    .add(
        "geo",
        [("广州", "上海"), ("广州", "北京"), ("广州", "杭州"), ("广州", "重庆")],
        type_=ChartType.LINES,
        effect_opts=opts.EffectOpts(
            symbol=SymbolType.ARROW, symbol_size=6, color="blue"
        ),
        linestyle_opts=opts.LineStyleOpts(curve=0.2),
    )
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(title_opts=opts.TitleOpts(title=""))
    .render("地理坐标图（4）.html")
)

（二）运行效果图：

Python代码：

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

c = (
    Map()
    .add("世界地图", [list(z) for z in zip(Faker.country, Faker.values())], "world")
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="世界地图（平面）"),
        visualmap_opts=opts.VisualMapOpts(max_=200),
    )
    .render("世界地图（平面）.html")
)

十：K线图烛台

（一）运行效果图：

Python代码：

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

data = [
    [2320.26, 2320.26, 2287.3, 2362.94],
    [2300, 2291.3, 2288.26, 2308.38],
    [2295.35, 2346.5, 2295.35, 2345.92],
    [2347.22, 2358.98, 2337.35, 2363.8],
    [2360.75, 2382.48, 2347.89, 2383.76],
    [2383.43, 2385.42, 2371.23, 2391.82],
    [2377.41, 2419.02, 2369.57, 2421.15],
    [2425.92, 2428.15, 2417.58, 2440.38],
    [2411, 2433.13, 2403.3, 2437.42],
    [2432.68, 2334.48, 2427.7, 2441.73],
    [2430.69, 2418.53, 2394.22, 2433.89],
    [2416.62, 2432.4, 2414.4, 2443.03],
    [2441.91, 2421.56, 2418.43, 2444.8],
    [2420.26, 2382.91, 2373.53, 2427.07],
    [2383.49, 2397.18, 2370.61, 2397.94],
    [2378.82, 2325.95, 2309.17, 2378.82],
    [2322.94, 2314.16, 2308.76, 2330.88],
    [2320.62, 2325.82, 2315.01, 2338.78],
    [2313.74, 2293.34, 2289.89, 2340.71],
    [2297.77, 2313.22, 2292.03, 2324.63],
    [2322.32, 2365.59, 2308.92, 2366.16],
    [2364.54, 2359.51, 2330.86, 2369.65],
    [2332.08, 2273.4, 2259.25, 2333.54],
    [2274.81, 2326.31, 2270.1, 2328.14],
    [2333.61, 2347.18, 2321.6, 2351.44],
    [2340.44, 2324.29, 2304.27, 2352.02],
    [2326.42, 2318.61, 2314.59, 2333.67],
    [2314.68, 2310.59, 2296.58, 2320.96],
    [2309.16, 2286.6, 2264.83, 2333.29],
    [2282.17, 2263.97, 2253.25, 2286.33],
    [2255.77, 2270.28, 2253.31, 2276.22],
]

c = (
    Kline()
    .add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)])
    .add_yaxis(
        "kline",
        data,
        markline_opts=opts.MarkLineOpts(
            data=[opts.MarkLineItem(type_="max", value_dim="close")]
        ),
    )
    .set_global_opts(
        xaxis_opts=opts.AxisOpts(is_scale=True),
        yaxis_opts=opts.AxisOpts(
            is_scale=True,
            splitarea_opts=opts.SplitAreaOpts(
                is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
            ),
        ),
        title_opts=opts.TitleOpts(title=""),
    )
    .render("K线图烛台（7）.html")
)

（二）运行效果图：

Python代码：

from typing import List, Sequence, Union
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Kline, Line, Bar, Grid

# 数据
echarts_data = [
    ["2015-10-16", 18.4, 18.58, 18.33, 18.79, 67.00, 1, 0.04, 0.11, 0.09],
    ["2015-10-19", 18.56, 18.25, 18.19, 18.56, 55.00, 0, -0.00, 0.08, 0.09],
    ["2015-10-20", 18.3, 18.22, 18.05, 18.41, 37.00, 0, 0.01, 0.09, 0.09],
    ["2015-10-21", 18.18, 18.69, 18.02, 18.98, 89.00, 0, 0.03, 0.10, 0.08],
    ["2015-10-22", 18.42, 18.29, 18.22, 18.48, 43.00, 0, -0.06, 0.05, 0.08],
    ["2015-10-23", 18.26, 18.19, 18.08, 18.36, 46.00, 0, -0.10, 0.03, 0.09],
    ["2015-10-26", 18.33, 18.07, 17.98, 18.35, 65.00, 0, -0.15, 0.03, 0.10],
    ["2015-10-27", 18.08, 18.04, 17.88, 18.13, 37.00, 0, -0.19, 0.03, 0.12],
    ["2015-10-28", 17.96, 17.86, 17.82, 17.99, 35.00, 0, -0.24, 0.03, 0.15],
    ["2015-10-29", 17.85, 17.81, 17.8, 17.93, 27.00, 0, -0.24, 0.06, 0.18],
    ["2015-10-30", 17.79, 17.93, 17.78, 18.08, 43.00, 0, -0.22, 0.11, 0.22],
    ["2015-11-02", 17.78, 17.83, 17.78, 18.04, 27.00, 0, -0.20, 0.15, 0.25],
    ["2015-11-03", 17.84, 17.9, 17.84, 18.06, 34.00, 0, -0.12, 0.22, 0.28],
    ["2015-11-04", 17.97, 18.36, 17.85, 18.39, 62.00, 0, -0.00, 0.30, 0.30],
    ["2015-11-05", 18.3, 18.57, 18.18, 19.08, 177.00, 0, 0.07, 0.33, 0.30],
    ["2015-11-06", 18.53, 18.68, 18.3, 18.71, 95.00, 0, 0.12, 0.35, 0.29],
    ["2015-11-09", 18.75, 19.08, 18.75, 19.98, 202.00, 1, 0.16, 0.35, 0.27],
    ["2015-11-10", 18.85, 18.64, 18.56, 18.99, 85.00, 0, 0.09, 0.29, 0.25],
    ["2015-11-11", 18.64, 18.44, 18.31, 18.64, 50.00, 0, 0.06, 0.27, 0.23],
    ["2015-11-12", 18.55, 18.27, 18.17, 18.57, 43.00, 0, 0.05, 0.25, 0.23],
    ["2015-11-13", 18.13, 18.14, 18.09, 18.34, 35.00, 0, 0.05, 0.24, 0.22],
    ["2015-11-16", 18.01, 18.1, 17.93, 18.17, 34.00, 0, 0.07, 0.25, 0.21],
    ["2015-11-17", 18.2, 18.14, 18.08, 18.45, 58.00, 0, 0.11, 0.25, 0.20],
    ["2015-11-18", 18.23, 18.16, 18.0, 18.45, 47.00, 0, 0.13, 0.25, 0.19],
    ["2015-11-19", 18.08, 18.2, 18.05, 18.25, 32.00, 0, 0.15, 0.24, 0.17],
    ["2015-11-20", 18.15, 18.15, 18.11, 18.29, 36.00, 0, 0.13, 0.21, 0.15],
    ["2015-11-23", 18.16, 18.19, 18.12, 18.34, 47.00, 0, 0.11, 0.18, 0.13],
    ["2015-11-24", 18.23, 17.88, 17.7, 18.23, 62.00, 0, 0.03, 0.13, 0.11],
    ["2015-11-25", 17.85, 17.73, 17.56, 17.85, 66.00, 0, -0.03, 0.09, 0.11],
    ["2015-11-26", 17.79, 17.53, 17.5, 17.92, 63.00, 0, -0.10, 0.06, 0.11],
    ["2015-11-27", 17.51, 17.04, 16.9, 17.51, 67.00, 0, -0.16, 0.05, 0.13],
    ["2015-11-30", 17.07, 17.2, 16.98, 17.32, 55.00, 0, -0.12, 0.09, 0.15],
    ["2015-12-01", 17.28, 17.11, 16.91, 17.28, 39.00, 0, -0.09, 0.12, 0.16],
    ["2015-12-02", 17.13, 17.91, 17.05, 17.99, 102.00, 0, -0.01, 0.17, 0.18],
    ["2015-12-03", 17.8, 17.78, 17.61, 17.98, 71.00, 0, -0.09, 0.14, 0.18],
    ["2015-12-04", 17.6, 17.25, 17.13, 17.69, 51.00, 0, -0.18, 0.10, 0.19],
    ["2015-12-07", 17.2, 17.39, 17.15, 17.45, 43.00, 0, -0.19, 0.12, 0.22],
    ["2015-12-08", 17.3, 17.42, 17.18, 17.62, 45.00, 0, -0.23, 0.13, 0.24],
    ["2015-12-09", 17.33, 17.39, 17.32, 17.59, 44.00, 0, -0.29, 0.13, 0.28],
    ["2015-12-10", 17.39, 17.26, 17.21, 17.65, 44.00, 0, -0.37, 0.13, 0.32],
    ["2015-12-11", 17.23, 16.92, 16.66, 17.26, 114.00, 1, -0.44, 0.15, 0.37],
    ["2015-12-14", 16.75, 17.06, 16.5, 17.09, 94.00, 0, -0.44, 0.21, 0.44],
    ["2015-12-15", 17.03, 17.03, 16.9, 17.06, 46.00, 0, -0.44, 0.28, 0.50],
    ["2015-12-16", 17.08, 16.96, 16.87, 17.09, 30.00, 0, -0.40, 0.36, 0.56],
    ["2015-12-17", 17.0, 17.1, 16.95, 17.12, 50.00, 0, -0.30, 0.47, 0.62],
    ["2015-12-18", 17.09, 17.52, 17.04, 18.06, 156.00, 0, -0.14, 0.59, 0.66],
    ["2015-12-21", 17.43, 18.23, 17.35, 18.45, 152.00, 1, 0.02, 0.69, 0.68],
    ["2015-12-22", 18.14, 18.27, 18.06, 18.32, 94.00, 0, 0.08, 0.72, 0.68],
    ["2015-12-23", 18.28, 18.19, 18.17, 18.71, 108.00, 0, 0.13, 0.73, 0.67],
    ["2015-12-24", 18.18, 18.14, 18.01, 18.31, 37.00, 0, 0.19, 0.74, 0.65],
    ["2015-12-25", 18.22, 18.33, 18.2, 18.36, 48.00, 0, 0.26, 0.75, 0.62],
    ["2015-12-28", 18.35, 17.84, 17.8, 18.39, 48.00, 0, 0.27, 0.72, 0.59],
    ["2015-12-29", 17.83, 17.94, 17.71, 17.97, 36.00, 0, 0.36, 0.73, 0.55],
    ["2015-12-30", 17.9, 18.26, 17.55, 18.3, 71.00, 1, 0.43, 0.71, 0.50],
    ["2015-12-31", 18.12, 17.99, 17.91, 18.33, 72.00, 0, 0.40, 0.63, 0.43],
    ["2016-01-04", 17.91, 17.28, 17.16, 17.95, 37.00, 1, 0.34, 0.55, 0.38],
    ["2016-01-05", 17.17, 17.23, 17.0, 17.55, 51.00, 0, 0.37, 0.51, 0.33],
    ["2016-01-06", 17.2, 17.31, 17.06, 17.33, 31.00, 0, 0.37, 0.46, 0.28],
    ["2016-01-07", 17.15, 16.67, 16.51, 17.15, 19.00, 0, 0.30, 0.37, 0.22],
    ["2016-01-08", 16.8, 16.81, 16.61, 17.06, 60.00, 0, 0.29, 0.32, 0.18],
    ["2016-01-11", 16.68, 16.04, 16.0, 16.68, 65.00, 0, 0.20, 0.24, 0.14],
    ["2016-01-12", 16.03, 15.98, 15.88, 16.25, 46.00, 0, 0.20, 0.21, 0.11],
    ["2016-01-13", 16.21, 15.87, 15.78, 16.21, 57.00, 0, 0.20, 0.18, 0.08],
    ["2016-01-14", 15.55, 15.89, 15.52, 15.96, 42.00, 0, 0.20, 0.16, 0.05],
    ["2016-01-15", 15.87, 15.48, 15.45, 15.92, 34.00, 1, 0.17, 0.11, 0.02],
    ["2016-01-18", 15.39, 15.42, 15.36, 15.7, 26.00, 0, 0.21, 0.10, -0.00],
    ["2016-01-19", 15.58, 15.71, 15.35, 15.77, 38.00, 0, 0.25, 0.09, -0.03],
    ["2016-01-20", 15.56, 15.52, 15.24, 15.68, 38.00, 0, 0.23, 0.05, -0.07],
    ["2016-01-21", 15.41, 15.3, 15.28, 15.68, 35.00, 0, 0.21, 0.00, -0.10],
    ["2016-01-22", 15.48, 15.28, 15.13, 15.49, 30.00, 0, 0.21, -0.02, -0.13],
    ["2016-01-25", 15.29, 15.48, 15.2, 15.49, 21.00, 0, 0.20, -0.06, -0.16],
    ["2016-01-26", 15.33, 14.86, 14.78, 15.39, 30.00, 0, 0.12, -0.13, -0.19],
    ["2016-01-27", 14.96, 15.0, 14.84, 15.22, 51.00, 0, 0.13, -0.14, -0.20],
    ["2016-01-28", 14.96, 14.72, 14.62, 15.06, 25.00, 0, 0.10, -0.17, -0.22],
    ["2016-01-29", 14.75, 14.99, 14.62, 15.08, 36.00, 0, 0.13, -0.17, -0.24],
    ["2016-02-01", 14.98, 14.72, 14.48, 15.18, 27.00, 0, 0.10, -0.21, -0.26],
    ["2016-02-02", 14.65, 14.85, 14.65, 14.95, 18.00, 0, 0.11, -0.21, -0.27],
    ["2016-02-03", 14.72, 14.67, 14.55, 14.8, 23.00, 0, 0.10, -0.24, -0.29],
    ["2016-02-04", 14.79, 14.88, 14.69, 14.93, 22.00, 0, 0.13, -0.24, -0.30],
    ["2016-02-05", 14.9, 14.86, 14.78, 14.93, 16.00, 0, 0.12, -0.26, -0.32],
    ["2016-02-15", 14.5, 14.66, 14.47, 14.82, 19.00, 0, 0.11, -0.28, -0.34],
    ["2016-02-16", 14.77, 14.94, 14.72, 15.05, 26.00, 0, 0.14, -0.28, -0.35],
    ["2016-02-17", 14.95, 15.03, 14.88, 15.07, 38.00, 0, 0.12, -0.31, -0.37],
    ["2016-02-18", 14.95, 14.9, 14.87, 15.06, 28.00, 0, 0.07, -0.35, -0.39],
    ["2016-02-19", 14.9, 14.75, 14.68, 14.94, 22.00, 0, 0.03, -0.38, -0.40],
    ["2016-02-22", 14.88, 15.01, 14.79, 15.11, 38.00, 1, 0.01, -0.40, -0.40],
    ["2016-02-23", 15.01, 14.83, 14.72, 15.01, 24.00, 0, -0.09, -0.45, -0.40],
    ["2016-02-24", 14.75, 14.81, 14.67, 14.87, 21.00, 0, -0.17, -0.48, -0.39],
    ["2016-02-25", 14.81, 14.25, 14.21, 14.81, 51.00, 1, -0.27, -0.50, -0.37],
    ["2016-02-26", 14.35, 14.45, 14.28, 14.57, 28.00, 0, -0.26, -0.46, -0.33],
    ["2016-02-29", 14.43, 14.56, 14.04, 14.6, 48.00, 0, -0.25, -0.41, -0.29],
    ["2016-03-01", 14.56, 14.65, 14.36, 14.78, 32.00, 0, -0.21, -0.36, -0.25],
    ["2016-03-02", 14.79, 14.96, 14.72, 14.97, 60.00, 0, -0.13, -0.29, -0.22],
    ["2016-03-03", 14.95, 15.15, 14.91, 15.19, 53.00, 1, -0.05, -0.23, -0.21],
    ["2016-03-04", 15.14, 15.97, 15.02, 16.02, 164.00, 1, 0.06, -0.17, -0.20],
    ["2016-03-07", 15.9, 15.78, 15.65, 16.0, 41.00, 0, 0.04, -0.19, -0.21],
    ["2016-03-08", 15.78, 15.96, 15.21, 15.99, 45.00, 0, 0.05, -0.19, -0.21],
    ["2016-03-09", 15.73, 16.05, 15.41, 16.08, 74.00, 0, 0.03, -0.20, -0.22],
    ["2016-03-10", 15.82, 15.66, 15.65, 15.98, 19.00, 0, -0.02, -0.23, -0.22],
    ["2016-03-11", 15.59, 15.76, 15.42, 15.78, 32.00, 0, 0.01, -0.22, -0.22],
    ["2016-03-14", 15.78, 15.72, 15.65, 16.04, 31.00, 0, 0.03, -0.20, -0.22],
    ["2016-03-15", 15.81, 15.86, 15.6, 15.99, 35.00, 0, 0.10, -0.18, -0.23],
    ["2016-03-16", 15.88, 16.42, 15.79, 16.45, 123.00, 0, 0.17, -0.16, -0.24],
    ["2016-03-17", 16.39, 16.23, 16.11, 16.4, 46.00, 0, 0.14, -0.20, -0.26],
    ["2016-03-18", 16.39, 16.17, 16.04, 16.4, 59.00, 0, 0.13, -0.22, -0.28],
    ["2016-03-21", 16.21, 16.22, 16.11, 16.44, 50.00, 0, 0.12, -0.24, -0.30],
    ["2016-03-22", 16.27, 16.19, 16.16, 16.42, 33.00, 0, 0.10, -0.27, -0.32],
    ["2016-03-23", 16.26, 16.18, 16.18, 16.29, 19.00, 0, 0.08, -0.30, -0.33],
    ["2016-03-24", 16.18, 16.11, 16.01, 16.23, 23.00, 0, 0.04, -0.33, -0.35],
    ["2016-03-25", 16.12, 16.13, 16.1, 16.2, 15.00, 0, 0.00, -0.35, -0.35],
    ["2016-03-28", 16.15, 15.85, 15.81, 16.2, 22.00, 0, -0.06, -0.38, -0.35],
    ["2016-03-29", 15.9, 15.79, 15.76, 16.05, 19.00, 0, -0.06, -0.37, -0.34],
    ["2016-03-30", 15.79, 16.24, 15.78, 16.3, 29.00, 0, -0.03, -0.35, -0.33],
    ["2016-03-31", 16.3, 16.09, 16.02, 16.35, 25.00, 0, -0.07, -0.37, -0.33],
    ["2016-04-01", 16.18, 16.27, 15.98, 16.3, 38.00, 0, -0.08, -0.36, -0.32],
    ["2016-04-05", 16.13, 16.34, 16.07, 16.37, 39.00, 0, -0.13, -0.37, -0.31],
    ["2016-04-06", 16.21, 16.26, 16.19, 16.35, 30.00, 0, -0.20, -0.39, -0.29],
    ["2016-04-07", 16.32, 16.1, 16.05, 16.35, 29.00, 1, -0.26, -0.39, -0.26],
    ["2016-04-08", 16.0, 16.16, 15.98, 16.21, 22.00, 0, -0.28, -0.37, -0.23],
    ["2016-04-11", 16.16, 16.31, 16.15, 16.57, 31.00, 0, -0.30, -0.33, -0.19],
    ["2016-04-12", 16.41, 16.29, 16.12, 16.41, 17.00, 0, -0.31, -0.30, -0.14],
    ["2016-04-13", 16.39, 16.48, 16.0, 16.68, 40.00, 0, -0.30, -0.25, -0.10],
    ["2016-04-14", 16.5, 16.46, 16.37, 16.68, 22.00, 0, -0.27, -0.19, -0.06],
    ["2016-04-15", 16.56, 16.93, 16.46, 17.04, 58.00, 0, -0.20, -0.12, -0.02],
    ["2016-04-18", 16.76, 17.06, 16.72, 17.27, 50.00, 0, -0.16, -0.07, 0.01],
    ["2016-04-19", 17.21, 17.11, 17.02, 17.23, 30.00, 0, -0.12, -0.02, 0.03],
    ["2016-04-20", 17.11, 17.33, 16.8, 17.36, 78.00, 0, -0.04, 0.03, 0.05],
    ["2016-04-21", 17.27, 17.69, 17.17, 17.93, 79.00, 0, 0.05, 0.08, 0.06],
    ["2016-04-22", 17.6, 17.87, 17.56, 18.02, 55.00, 0, 0.09, 0.10, 0.05],
    ["2016-04-25", 17.75, 17.9, 17.41, 17.96, 39.00, 1, 0.11, 0.09, 0.04],
    ["2016-04-26", 17.81, 17.91, 17.6, 17.95, 39.00, 0, 0.12, 0.08, 0.02],
    ["2016-04-27", 17.9, 17.88, 17.81, 17.95, 25.00, 0, 0.12, 0.06, 0.00],
    ["2016-04-28", 17.93, 17.88, 17.67, 17.93, 28.00, 0, 0.11, 0.04, -0.01],
    ["2016-04-29", 17.87, 17.75, 17.73, 17.92, 19.00, 0, 0.08, 0.01, -0.03],
    ["2016-05-03", 17.79, 17.7, 17.56, 17.85, 35.00, 0, 0.05, -0.01, -0.04],
    ["2016-05-04", 17.7, 17.65, 17.59, 17.71, 24.00, 0, 0.02, -0.04, -0.05],
    ["2016-05-05", 17.65, 17.62, 17.46, 17.7, 20.00, 0, -0.03, -0.06, -0.05],
    ["2016-05-06", 17.62, 17.32, 17.3, 17.65, 29.00, 0, -0.10, -0.09, -0.05],
    ["2016-05-09", 17.33, 17.3, 17.21, 17.45, 23.00, 0, -0.13, -0.10, -0.03],
    ["2016-05-10", 17.11, 17.04, 16.98, 17.41, 28.00, 0, -0.15, -0.09, -0.01],
    ["2016-05-11", 17.06, 17.15, 17.06, 17.32, 20.00, 0, -0.12, -0.05, 0.01],
    ["2016-05-12", 17.02, 17.46, 17.02, 17.58, 26.00, 0, -0.07, -0.01, 0.03],
    ["2016-05-13", 17.41, 17.57, 17.34, 17.62, 23.00, 0, -0.06, 0.01, 0.03],
    ["2016-05-16", 17.55, 17.5, 17.48, 17.64, 37.00, 0, -0.06, 0.01, 0.04],
    ["2016-05-17", 17.49, 17.48, 17.39, 17.53, 13.00, 0, -0.03, 0.03, 0.05],
    ["2016-05-18", 17.41, 17.82, 17.39, 17.87, 46.00, 0, 0.01, 0.06, 0.06],
    ["2016-05-19", 17.74, 17.81, 17.67, 17.86, 17.00, 0, -0.01, 0.05, 0.05],
    ["2016-05-20", 17.76, 17.88, 17.7, 17.93, 14.00, 0, -0.03, 0.04, 0.06],
    ["2016-05-23", 17.88, 17.52, 17.48, 17.97, 16.00, 0, -0.09, 0.02, 0.06],
    ["2016-05-24", 17.51, 17.33, 17.32, 17.51, 8.00, 0, -0.09, 0.03, 0.07],
    ["2016-05-25", 17.59, 17.55, 17.44, 17.59, 10.00, 0, -0.03, 0.07, 0.08],
    ["2016-05-26", 17.5, 17.69, 17.5, 17.8, 12.00, 0, 0.00, 0.09, 0.09],
    ["2016-05-27", 17.77, 17.66, 17.62, 17.77, 7.00, 0, 0.03, 0.10, 0.09],
    ["2016-05-30", 17.75, 17.84, 17.62, 17.87, 20.00, 0, 0.08, 0.12, 0.08],
    ["2016-05-31", 17.88, 18.0, 17.81, 18.03, 41.00, 0, 0.10, 0.12, 0.07],
    ["2016-06-01", 18.09, 17.83, 17.73, 18.09, 22.00, 0, 0.08, 0.10, 0.06],
    ["2016-06-02", 17.82, 17.73, 17.66, 17.88, 10.00, 0, 0.07, 0.08, 0.05],
    ["2016-06-03", 17.8, 17.78, 17.71, 17.83, 9.00, 0, 0.08, 0.08, 0.04],
    ["2016-06-06", 17.73, 17.64, 17.56, 17.83, 12.00, 0, 0.07, 0.06, 0.03],
    ["2016-06-07", 17.76, 17.8, 17.59, 17.87, 11.00, 0, 0.08, 0.06, 0.02],
    ["2016-06-08", 17.75, 17.77, 17.65, 17.84, 9.00, 0, 0.04, 0.03, 0.01],
    ["2016-06-13", 17.58, 17.32, 17.29, 17.79, 16.00, 0, -0.02, -0.01, 0.00],
    ["2016-06-14", 17.33, 17.38, 17.29, 17.5, 10.00, 0, -0.01, 0.00, 0.00],
    ["2016-06-15", 17.25, 17.39, 17.25, 17.46, 18.00, 0, 0.00, 0.01, 0.00],
    ["2016-06-16", 17.26, 17.4, 17.26, 17.46, 22.00, 0, 0.01, 0.01, 0.00],
    ["2016-06-17", 17.38, 17.5, 17.37, 17.67, 13.00, 0, 0.03, 0.02, 0.00],
    ["2016-06-20", 17.62, 17.51, 17.42, 17.63, 15.00, 0, 0.03, 0.01, -0.00],
    ["2016-06-21", 17.53, 17.54, 17.5, 17.7, 11.00, 0, 0.02, 0.00, -0.01],
    ["2016-06-22", 17.5, 17.5, 17.46, 17.6, 10.00, 0, -0.01, -0.01, -0.01],
    ["2016-06-23", 17.52, 17.26, 17.24, 17.53, 16.00, 0, -0.04, -0.03, -0.01],
    ["2016-06-24", 17.26, 17.25, 17.18, 17.38, 60.00, 0, -0.03, -0.02, -0.00],
    ["2016-06-27", 17.25, 17.28, 17.18, 17.33, 19.00, 0, -0.01, -0.00, 0.00],
    ["2016-06-28", 17.25, 17.29, 17.21, 17.32, 13.00, 0, 0.02, 0.01, 0.00],
    ["2016-06-29", 17.31, 17.45, 17.27, 17.49, 21.00, 0, 0.07, 0.04, 0.00],
    ["2016-06-30", 17.47, 17.5, 17.39, 17.55, 17.00, 0, 0.11, 0.04, -0.01],
    ["2016-07-01", 17.5, 17.63, 17.49, 17.66, 10.00, 0, 0.14, 0.05, -0.03],
    ["2016-07-04", 17.63, 17.72, 17.63, 17.92, 17.00, 0, 0.16, 0.03, -0.05],
    ["2016-07-05", 17.79, 17.56, 17.45, 17.79, 18.00, 0, 0.14, 0.00, -0.07],
    ["2016-07-06", 17.53, 17.42, 17.31, 17.54, 20.00, 0, 0.14, -0.02, -0.09],
    ["2016-07-07", 17.41, 17.51, 17.35, 17.52, 15.00, 0, 0.16, -0.03, -0.11],
    ["2016-07-08", 17.5, 17.39, 17.35, 17.51, 15.00, 0, 0.16, -0.05, -0.13],
    ["2016-07-11", 17.49, 17.48, 17.4, 17.55, 16.00, 0, 0.17, -0.07, -0.15],
    ["2016-07-12", 17.48, 17.71, 17.46, 17.75, 25.00, 0, 0.16, -0.10, -0.18],
    ["2016-07-13", 17.13, 17.05, 17.02, 17.39, 28.00, 0, 0.07, -0.17, -0.20],
    ["2016-07-14", 17.07, 17.09, 17.0, 17.16, 12.00, 0, 0.08, -0.17, -0.21],
    ["2016-07-15", 17.08, 17.14, 17.08, 17.17, 11.00, 0, 0.09, -0.18, -0.22],
    ["2016-07-18", 17.15, 17.26, 17.13, 17.49, 24.00, 0, 0.10, -0.19, -0.23],
    ["2016-07-19", 17.26, 17.12, 17.09, 17.33, 13.00, 0, 0.07, -0.21, -0.25],
    ["2016-07-20", 17.1, 17.07, 17.02, 17.14, 11.00, 0, 0.06, -0.23, -0.26],
    ["2016-07-21", 17.07, 17.24, 17.07, 17.27, 14.00, 0, 0.07, -0.23, -0.27],
    ["2016-07-22", 17.25, 17.08, 17.03, 17.25, 10.00, 0, 0.04, -0.26, -0.28],
    ["2016-07-25", 17.09, 17.12, 17.01, 17.18, 8.00, 0, 0.04, -0.26, -0.28],
    ["2016-07-26", 17.05, 17.17, 17.05, 17.2, 11.00, 0, 0.04, -0.27, -0.29],
    ["2016-07-27", 17.2, 17.37, 16.89, 17.38, 32.00, 0, 0.02, -0.28, -0.29],
    ["2016-07-28", 17.19, 17.14, 17.09, 17.29, 19.00, 0, -0.04, -0.32, -0.30],
    ["2016-07-29", 17.15, 17.16, 17.04, 17.23, 12.00, 0, -0.08, -0.33, -0.29],
    ["2016-08-01", 17.15, 17.18, 17.1, 17.24, 19.00, 0, -0.13, -0.34, -0.28],
    ["2016-08-02", 17.21, 17.15, 17.12, 17.25, 9.00, 0, -0.19, -0.36, -0.26],
    ["2016-08-03", 17.08, 17.07, 17.01, 17.16, 9.00, 0, -0.25, -0.36, -0.24],
    ["2016-08-04", 17.11, 17.06, 16.98, 17.12, 11.00, 1, -0.29, -0.35, -0.20],
    ["2016-08-05", 17.06, 17.1, 17.05, 17.15, 16.00, 0, -0.33, -0.32, -0.16],
    ["2016-08-08", 17.14, 17.13, 17.07, 17.15, 13.00, 0, -0.35, -0.29, -0.11],
    ["2016-08-09", 17.13, 17.17, 17.1, 17.2, 25.00, 0, -0.35, -0.24, -0.06],
    ["2016-08-10", 17.17, 17.28, 17.15, 17.29, 18.00, 0, -0.31, -0.17, -0.01],
    ["2016-08-11", 17.3, 17.45, 17.26, 17.87, 31.00, 0, -0.24, -0.09, 0.03],
    ["2016-08-12", 17.51, 17.99, 17.47, 18.0, 44.00, 0, -0.14, -0.00, 0.07],
    ["2016-08-15", 18.1, 18.42, 18.02, 18.99, 81.00, 0, -0.09, 0.04, 0.09],
    ["2016-08-16", 18.64, 18.31, 18.12, 18.87, 60.00, 0, -0.10, 0.05, 0.10],
    ["2016-08-17", 18.43, 18.4, 18.31, 18.68, 21.00, 0, -0.08, 0.08, 0.11],
    ["2016-08-18", 18.33, 18.23, 18.13, 18.65, 32.00, 0, -0.07, 0.09, 0.13],
    ["2016-08-19", 18.34, 18.62, 18.31, 18.75, 39.00, 0, 0.00, 0.14, 0.14],
    ["2016-08-22", 18.62, 18.69, 18.51, 18.8, 20.00, 0, 0.01, 0.14, 0.13],
    ["2016-08-23", 18.61, 18.66, 18.52, 19.0, 28.00, 0, 0.01, 0.14, 0.13],
    ["2016-08-24", 18.66, 18.62, 18.43, 18.7, 19.00, 0, 0.00, 0.13, 0.13],
    ["2016-08-25", 18.57, 18.51, 18.19, 18.64, 19.00, 0, -0.00, 0.13, 0.13],
    ["2016-08-26", 18.49, 18.55, 18.44, 18.6, 16.00, 0, 0.01, 0.13, 0.13],
    ["2016-08-29", 18.46, 18.27, 18.03, 18.48, 20.00, 0, 0.01, 0.13, 0.13],
    ["2016-08-30", 18.24, 18.44, 18.23, 18.52, 19.00, 0, 0.07, 0.17, 0.13],
    ["2016-08-31", 18.36, 18.63, 18.36, 18.76, 15.00, 0, 0.13, 0.18, 0.12],
    ["2016-09-01", 18.6, 18.62, 18.55, 18.78, 15.00, 0, 0.16, 0.18, 0.10],
    ["2016-09-02", 18.52, 18.68, 18.48, 18.72, 17.00, 0, 0.19, 0.17, 0.08],
    ["2016-09-05", 18.68, 18.75, 18.57, 18.82, 19.00, 0, 0.20, 0.15, 0.05],
    ["2016-09-06", 18.75, 18.51, 18.43, 18.78, 17.00, 0, 0.18, 0.11, 0.02],
    ["2016-09-07", 18.51, 18.56, 18.4, 18.62, 17.00, 0, 0.17, 0.08, -0.00],
    ["2016-09-08", 18.58, 18.53, 18.48, 18.63, 8.00, 0, 0.13, 0.04, -0.03],
    ["2016-09-09", 18.52, 18.33, 18.31, 18.57, 8.00, 0, 0.06, -0.02, -0.05],
    ["2016-09-12", 18.16, 17.9, 17.81, 18.18, 28.00, 0, -0.02, -0.07, -0.06],
    ["2016-09-13", 17.91, 17.91, 17.9, 18.08, 13.00, 0, -0.05, -0.08, -0.05],
    ["2016-09-14", 17.99, 17.54, 17.48, 17.99, 22.00, 0, -0.09, -0.09, -0.05],
    ["2016-09-19", 17.55, 17.81, 17.55, 17.88, 16.00, 0, -0.06, -0.06, -0.03],
    ["2016-09-20", 17.8, 17.74, 17.67, 17.85, 10.00, 0, -0.06, -0.05, -0.02],
    ["2016-09-21", 17.75, 17.88, 17.75, 17.95, 7.00, 0, -0.03, -0.03, -0.02],
    ["2016-09-22", 17.99, 17.97, 17.88, 18.17, 12.00, 0, -0.02, -0.02, -0.01],
    ["2016-09-23", 17.99, 17.98, 17.93, 18.09, 13.00, 0, -0.01, -0.01, -0.01],
    ["2016-09-26", 17.91, 18.0, 17.85, 18.09, 14.00, 0, -0.00, -0.01, -0.01],
    ["2016-09-27", 17.97, 18.07, 17.94, 18.1, 10.00, 0, 0.00, -0.01, -0.01],
    ["2016-09-28", 18.06, 17.89, 17.83, 18.06, 10.00, 0, -0.00, -0.01, -0.01],
    ["2016-09-29", 17.96, 18.0, 17.92, 18.07, 10.00, 0, 0.03, 0.01, -0.01],
    ["2016-09-30", 17.96, 18.0, 17.95, 18.1, 8.00, 0, 0.06, 0.02, -0.01],
    ["2016-10-10", 18.03, 18.3, 18.03, 18.38, 19.00, 0, 0.11, 0.04, -0.02],
    ["2016-10-11", 18.33, 18.33, 18.26, 18.49, 12.00, 0, 0.10, 0.02, -0.04],
    ["2016-10-12", 18.28, 18.15, 18.1, 18.31, 10.00, 0, 0.07, -0.02, -0.05],
    ["2016-10-13", 18.15, 18.09, 18.05, 18.21, 10.00, 0, 0.06, -0.03, -0.06],
    ["2016-10-14", 18.1, 18.1, 18.0, 18.15, 12.00, 0, 0.04, -0.05, -0.07],
    ["2016-10-17", 18.07, 17.86, 17.83, 18.1, 12.00, 0, 0.01, -0.07, -0.08],
    ["2016-10-18", 17.86, 17.93, 17.84, 17.99, 14.00, 0, 0.03, -0.07, -0.08],
    ["2016-10-19", 17.93, 17.88, 17.83, 18.05, 11.00, 0, 0.03, -0.07, -0.08],
    ["2016-10-20", 17.9, 17.89, 17.83, 17.98, 12.00, 0, 0.05, -0.06, -0.09],
    ["2016-10-21", 17.91, 17.91, 17.82, 17.93, 12.00, 0, 0.07, -0.06, -0.09],
    ["2016-10-24", 17.93, 18.31, 17.86, 18.42, 29.00, 0, 0.11, -0.05, -0.10],
    ["2016-10-25", 18.31, 18.13, 18.09, 18.46, 19.00, 0, 0.06, -0.09, -0.12],
    ["2016-10-26", 18.12, 17.97, 17.95, 18.15, 14.00, 0, 0.02, -0.12, -0.13],
    ["2016-10-27", 18.06, 17.81, 17.77, 18.06, 21.00, 0, -0.01, -0.13, -0.13],
    ["2016-10-28", 17.8, 17.9, 17.8, 18.05, 20.00, 0, -0.01, -0.13, -0.13],
    ["2016-10-31", 17.87, 17.86, 17.72, 17.96, 12.00, 0, -0.02, -0.14, -0.13],
    ["2016-11-01", 17.87, 17.98, 17.79, 17.99, 18.00, 0, -0.03, -0.14, -0.12],
    ["2016-11-02", 17.86, 17.84, 17.76, 17.94, 30.00, 0, -0.06, -0.15, -0.12],
    ["2016-11-03", 17.83, 17.93, 17.79, 17.97, 27.00, 0, -0.07, -0.14, -0.11],
    ["2016-11-04", 17.9, 17.91, 17.87, 18.0, 26.00, 0, -0.09, -0.15, -0.10],
    ["2016-11-07", 17.91, 17.89, 17.85, 17.93, 20.00, 0, -0.11, -0.14, -0.09],
    ["2016-11-08", 17.92, 17.99, 17.89, 18.06, 26.00, 0, -0.12, -0.13, -0.07],
    ["2016-11-09", 18.0, 17.89, 17.77, 18.08, 34.00, 0, -0.15, -0.13, -0.06],
    ["2016-11-10", 17.95, 18.0, 17.94, 18.11, 27.00, 0, -0.15, -0.11, -0.03],
    ["2016-11-11", 17.95, 18.02, 17.93, 18.08, 27.00, 0, -0.17, -0.10, -0.01],
    ["2016-11-14", 18.0, 18.04, 17.95, 18.25, 35.00, 0, -0.18, -0.08, 0.01],
    ["2016-11-15", 18.1, 18.18, 18.03, 18.24, 25.00, 0, -0.18, -0.06, 0.04],
    ["2016-11-16", 18.23, 18.12, 18.05, 18.29, 23.00, 0, -0.21, -0.04, 0.06],
    ["2016-11-17", 18.11, 18.12, 18.01, 18.14, 27.00, 0, -0.21, -0.01, 0.09],
    ["2016-11-18", 18.12, 18.1, 18.03, 18.16, 18.00, 0, -0.19, 0.03, 0.12],
    ["2016-11-21", 18.08, 18.34, 18.08, 18.68, 41.00, 0, -0.13, 0.08, 0.15],
    ["2016-11-22", 18.37, 18.37, 18.28, 18.49, 52.00, 0, -0.09, 0.12, 0.17],
    ["2016-11-23", 18.4, 18.84, 18.37, 18.9, 66.00, 0, -0.02, 0.17, 0.18],
    ["2016-11-24", 18.77, 18.74, 18.61, 18.97, 26.00, 0, -0.02, 0.17, 0.18],
    ["2016-11-25", 18.8, 18.99, 18.66, 19.02, 40.00, 0, -0.01, 0.18, 0.19],
    ["2016-11-28", 19.1, 18.65, 18.52, 19.2, 85.00, 0, -0.06, 0.16, 0.19],
    ["2016-11-29", 18.65, 18.75, 18.51, 18.76, 49.00, 0, -0.06, 0.17, 0.20],
    ["2016-11-30", 18.76, 18.55, 18.47, 18.82, 39.00, 0, -0.08, 0.17, 0.21],
    ["2016-12-01", 18.55, 18.49, 18.41, 18.64, 53.00, 0, -0.06, 0.19, 0.22],
    ["2016-12-02", 18.53, 18.49, 18.24, 18.54, 48.00, 0, -0.02, 0.21, 0.23],
    ["2016-12-05", 18.39, 18.66, 18.34, 18.67, 50.00, 0, 0.03, 0.25, 0.23],
    ["2016-12-06", 18.66, 18.6, 18.57, 18.78, 31.00, 0, 0.08, 0.26, 0.23],
    ["2016-12-07", 18.65, 18.62, 18.58, 18.71, 12.00, 0, 0.15, 0.29, 0.21],
    ["2016-12-08", 18.67, 18.76, 18.62, 18.88, 26.00, 0, 0.25, 0.32, 0.19],
    ["2016-12-09", 18.76, 19.2, 18.75, 19.34, 62.00, 0, 0.34, 0.33, 0.16],
    ["2016-12-12", 19.16, 19.25, 18.9, 19.65, 79.00, 1, 0.34, 0.28, 0.11],
    ["2016-12-13", 19.09, 18.88, 18.81, 19.2, 24.00, 0, 0.27, 0.20, 0.06],
    ["2016-12-14", 18.8, 18.82, 18.8, 19.14, 32.00, 0, 0.23, 0.13, 0.02],
    ["2016-12-15", 18.73, 18.24, 18.2, 18.73, 36.00, 0, 0.13, 0.05, -0.01],
    ["2016-12-16", 18.24, 18.18, 18.12, 18.4, 24.00, 0, 0.10, 0.02, -0.03],
    ["2016-12-19", 18.15, 18.01, 17.93, 18.18, 24.00, 0, 0.06, -0.02, -0.05],
    ["2016-12-20", 17.99, 17.79, 17.7, 17.99, 29.00, 1, 0.02, -0.05, -0.05],
    ["2016-12-21", 17.83, 17.81, 17.77, 17.98, 30.00, 0, 0.00, -0.05, -0.06],
    ["2016-12-22", 17.85, 17.72, 17.65, 17.85, 21.00, 0, -0.03, -0.07, -0.06],
    ["2016-12-23", 17.77, 17.6, 17.54, 17.77, 18.00, 0, -0.04, -0.08, -0.05],
    ["2016-12-26", 17.56, 17.75, 17.39, 17.77, 16.00, 0, -0.04, -0.07, -0.05],
    ["2016-12-27", 17.73, 17.71, 17.65, 17.82, 10.00, 0, -0.06, -0.07, -0.04],
    ["2016-12-28", 17.72, 17.62, 17.49, 17.77, 26.00, 0, -0.09, -0.07, -0.03],
    ["2016-12-29", 17.6, 17.49, 17.43, 17.62, 28.00, 0, -0.09, -0.06, -0.02],
    ["2016-12-30", 17.53, 17.6, 17.47, 17.61, 22.00, 0, -0.05, -0.03, -0.01],
    ["2017-01-03", 17.6, 17.92, 17.57, 17.98, 28.00, 1, 0.00, 0.00, 0.00],
]


def split_data(origin_data) -> dict:
    datas = []
    times = []
    vols = []
    macds = []
    difs = []
    deas = []

    for i in range(len(origin_data)):
        datas.append(origin_data[i][1:])
        times.append(origin_data[i][0:1][0])
        vols.append(origin_data[i][5])
        macds.append(origin_data[i][7])
        difs.append(origin_data[i][8])
        deas.append(origin_data[i][9])
    vols = [int(v) for v in vols]

    return {
        "datas": datas,
        "times": times,
        "vols": vols,
        "macds": macds,
        "difs": difs,
        "deas": deas,
    }


def split_data_part() -> Sequence:
    mark_line_data = []
    idx = 0
    tag = 0
    vols = 0
    for i in range(len(data["times"])):
        if data["datas"][i][5] != 0 and tag == 0:
            idx = i
            vols = data["datas"][i][4]
            tag = 1
        if tag == 1:
            vols += data["datas"][i][4]
        if data["datas"][i][5] != 0 or tag == 1:
            mark_line_data.append(
                [
                    {
                        "xAxis": idx,
                        "yAxis": float("%.2f" % data["datas"][idx][3])
                        if data["datas"][idx][1] > data["datas"][idx][0]
                        else float("%.2f" % data["datas"][idx][2]),
                        "value": vols,
                    },
                    {
                        "xAxis": i,
                        "yAxis": float("%.2f" % data["datas"][i][3])
                        if data["datas"][i][1] > data["datas"][i][0]
                        else float("%.2f" % data["datas"][i][2]),
                    },
                ]
            )
            idx = i
            vols = data["datas"][i][4]
            tag = 2
        if tag == 2:
            vols += data["datas"][i][4]
        if data["datas"][i][5] != 0 and tag == 2:
            mark_line_data.append(
                [
                    {
                        "xAxis": idx,
                        "yAxis": float("%.2f" % data["datas"][idx][3])
                        if data["datas"][i][1] > data["datas"][i][0]
                        else float("%.2f" % data["datas"][i][2]),
                        "value": str(float("%.2f" % (vols / (i - idx + 1)))) + " M",
                    },
                    {
                        "xAxis": i,
                        "yAxis": float("%.2f" % data["datas"][i][3])
                        if data["datas"][i][1] > data["datas"][i][0]
                        else float("%.2f" % data["datas"][i][2]),
                    },
                ]
            )
            idx = i
            vols = data["datas"][i][4]
    return mark_line_data


def calculate_ma(day_count: int):
    result: List[Union[float, str]] = []

    for i in range(len(data["times"])):
        if i < day_count:
            result.append("-")
            continue
        sum_total = 0.0
        for j in range(day_count):
            sum_total += float(data["datas"][i - j][1])
        result.append(abs(float("%.2f" % (sum_total / day_count))))
    return result


def draw_chart():
    kline = (
        Kline()
        .add_xaxis(xaxis_data=data["times"])
        .add_yaxis(
            series_name="",
            y_axis=data["datas"],
            itemstyle_opts=opts.ItemStyleOpts(
                color="#ef232a",
                color0="#14b143",
                border_color="#ef232a",
                border_color0="#14b143",
            ),
            markpoint_opts=opts.MarkPointOpts(
                data=[
                    opts.MarkPointItem(type_="max", name="最大值"),
                    opts.MarkPointItem(type_="min", name="最小值"),
                ]
            ),
            markline_opts=opts.MarkLineOpts(
                label_opts=opts.LabelOpts(
                    position="middle", color="blue", font_size=15
                ),
                data=split_data_part(),
                symbol=["circle", "none"],
            ),
        )
        .set_series_opts(
            markarea_opts=opts.MarkAreaOpts(is_silent=True, data=split_data_part())
        )
        .set_global_opts(
            title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
            xaxis_opts=opts.AxisOpts(
                type_="category",
                is_scale=True,
                boundary_gap=False,
                axisline_opts=opts.AxisLineOpts(is_on_zero=False),
                splitline_opts=opts.SplitLineOpts(is_show=False),
                split_number=20,
                min_="dataMin",
                max_="dataMax",
            ),
            yaxis_opts=opts.AxisOpts(
                is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True)
            ),
            tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
            datazoom_opts=[
                opts.DataZoomOpts(
                    is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
                ),
                opts.DataZoomOpts(
                    is_show=True, xaxis_index=[0, 1], pos_top="97%", range_end=100
                ),
                opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
            ],
            # 三个图的 axis 连在一块
            # axispointer_opts=opts.AxisPointerOpts(
            #     is_show=True,
            #     link=[{"xAxisIndex": "all"}],
            #     label=opts.LabelOpts(background_color="#777"),
            # ),
        )
    )

    kline_line = (
        Line()
        .add_xaxis(xaxis_data=data["times"])
        .add_yaxis(
            series_name="MA5",
            y_axis=calculate_ma(day_count=5),
            is_smooth=True,
            linestyle_opts=opts.LineStyleOpts(opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False),
        )
        .set_global_opts(
            xaxis_opts=opts.AxisOpts(
                type_="category",
                grid_index=1,
                axislabel_opts=opts.LabelOpts(is_show=False),
            ),
            yaxis_opts=opts.AxisOpts(
                grid_index=1,
                split_number=3,
                axisline_opts=opts.AxisLineOpts(is_on_zero=False),
                axistick_opts=opts.AxisTickOpts(is_show=False),
                splitline_opts=opts.SplitLineOpts(is_show=False),
                axislabel_opts=opts.LabelOpts(is_show=True),
            ),
        )
    )
    # Overlap Kline + Line
    overlap_kline_line = kline.overlap(kline_line)

    # Bar-1
    bar_1 = (
        Bar()
        .add_xaxis(xaxis_data=data["times"])
        .add_yaxis(
            series_name="Volumn",
            y_axis=data["vols"],
            xaxis_index=1,
            yaxis_index=1,
            label_opts=opts.LabelOpts(is_show=False),

            itemstyle_opts=opts.ItemStyleOpts(
                color=JsCode(
                    """
                function(params) {
                    var colorList;
                    if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
                        colorList = '#ef232a';
                    } else {
                        colorList = '#14b143';
                    }
                    return colorList;
                }
                """
                )
            ),
        )
        .set_global_opts(
            xaxis_opts=opts.AxisOpts(
                type_="category",
                grid_index=1,
                axislabel_opts=opts.LabelOpts(is_show=False),
            ),
            legend_opts=opts.LegendOpts(is_show=False),
        )
    )

    # Bar-2 (Overlap Bar + Line)
    bar_2 = (
        Bar()
        .add_xaxis(xaxis_data=data["times"])
        .add_yaxis(
            series_name="MACD",
            y_axis=data["macds"],
            xaxis_index=2,
            yaxis_index=2,
            label_opts=opts.LabelOpts(is_show=False),
            itemstyle_opts=opts.ItemStyleOpts(
                color=JsCode(
                    """
                        function(params) {
                            var colorList;
                            if (params.data >= 0) {
                              colorList = '#ef232a';
                            } else {
                              colorList = '#14b143';
                            }
                            return colorList;
                        }
                        """
                )
            ),
        )
        .set_global_opts(
            xaxis_opts=opts.AxisOpts(
                type_="category",
                grid_index=2,
                axislabel_opts=opts.LabelOpts(is_show=False),
            ),
            yaxis_opts=opts.AxisOpts(
                grid_index=2,
                split_number=4,
                axisline_opts=opts.AxisLineOpts(is_on_zero=False),
                axistick_opts=opts.AxisTickOpts(is_show=False),
                splitline_opts=opts.SplitLineOpts(is_show=False),
                axislabel_opts=opts.LabelOpts(is_show=True),
            ),
            legend_opts=opts.LegendOpts(is_show=False),
        )
    )

    line_2 = (
        Line()
        .add_xaxis(xaxis_data=data["times"])
        .add_yaxis(
            series_name="DIF",
            y_axis=data["difs"],
            xaxis_index=2,
            yaxis_index=2,
            label_opts=opts.LabelOpts(is_show=False),
        )
        .add_yaxis(
            series_name="DIF",
            y_axis=data["deas"],
            xaxis_index=2,
            yaxis_index=2,
            label_opts=opts.LabelOpts(is_show=False),
        )
        .set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
    )
    # 最下面的柱状图和折线图
    overlap_bar_line = bar_2.overlap(line_2)

    # 最后的 Grid
    grid_chart = Grid(init_opts=opts.InitOpts(width="1400px", height="800px"))

    # demo 中的代码也是用全局变量传的
    grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))

    # K线图和 MA5 的折线图
    grid_chart.add(
        overlap_kline_line,
        grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
    )
    # Volumn 柱状图
    grid_chart.add(
        bar_1,
        grid_opts=opts.GridOpts(
            pos_left="3%", pos_right="1%", pos_top="71%", height="10%"
        ),
    )
    # MACD DIFS DEAS
    grid_chart.add(
        overlap_bar_line,
        grid_opts=opts.GridOpts(
            pos_left="3%", pos_right="1%", pos_top="82%", height="14%"
        ),
    )
    grid_chart.render("K线图烛台（10）.html")


if __name__ == "__main__":
    data = split_data(origin_data=echarts_data)
    draw_chart()

注意：由于HTML代码量太大，所以以上代码只写了Python代码

且以上只是可视化实例图其中的一小部分，完整的可视化实例图及其源代码我已上传至资源文件，待审核完成后大家就可以自行下载了