【Web开发】Python实现Web图表功能（pyecharts入门学习）- 汇维网

”柳丝榆荚自芳菲，不管桃飘与李飞；”,,, 新版本系列将从 v1.0.0 开始，文档位于 pyecharts.org；示例位于 gallery.pyecharts.org,,,,,,https://jupyter.org/try-jupyter/retro/notebooks/?path=notebooks/Intro.ipynb
https://jupyter.org/install,,,编辑代码：
预览成果：
,Install JupyterLab with pip:, Once installed, launch JupyterLab with:,
鼠标点击NoteBook按钮，进入编辑界面，并输入代码如下如下：

, 浏览器访问：http://localhost:8866/
查看某个ipynb文件如下：
,,,,,,,,,如果您觉得该方法或代码有一点点用处，可以给作者点个赞，或打赏杯咖啡；╮(￣▽￣)╭
如果您感觉方法或代码不咋地//(ㄒoㄒ)//
，就在评论处留言，作者继续改进；o_O???
如果您需要相关功能的代码定制化开发，可以留言私信作者；(◡‿◡)
感谢各位大佬童鞋们的支持！( ´ ▽´ )ﾉ ( ´ ▽´)っ！！！,”侬今葬花人笑痴，他年葬侬知是谁？”, 浏览器访问：http://localhost:8866/
查看某个ipynb文件如下：
,

”柳丝榆荚自芳菲，不管桃飘与李飞；”

1、简介

A Python Echarts Plotting Library. Apache Echarts 是一个由百度开源的数据可视化，凭借着良好的交互性，精巧的图表设计，得到了众多开发者的认可。而 Python 是一门富有表达力的语言，很适合用于数据处理。当数据分析遇上数据可视化时，pyecharts 诞生了。

官网地址： https://pyecharts.org/#/ https://gallery.pyecharts.org/#/
github地址： https://github.com/pyecharts/pyecharts/ https://github.com/pyecharts/pyecharts-gallery

简洁的 API 设计，使用如丝滑般流畅，支持链式调用
囊括了 30+ 种常见图表，应有尽有
支持主流 Notebook 环境，Jupyter Notebook 和 JupyterLab
可轻松集成至 Flask，Sanic，Django 等主流 Web 框架
高度灵活的配置项，可轻松搭配出精美的图表
详细的文档和示例，帮助开发者更快的上手项目
多达 400+ 地图文件，并且支持原生百度地图，为地理数据可视化提供强有力的支持

2、下载和安装

新版本系列将从 v1.0.0 开始，文档位于 pyecharts.org；示例位于 gallery.pyecharts.org

2.1 pip 安装

# 安装 v1 以上版本
$ pip install pyecharts -U

# 如果需要安装 0.5.11 版本的开发者，可以使用
# pip install pyecharts==0.5.11

2.2 源码安装

# 安装 v1 以上版本
$ git clone https://github.com/pyecharts/pyecharts.git
# 如果需要安装 0.5.11 版本，请使用 git clone https://github.com/pyecharts/pyecharts.git -b v05x
$ cd pyecharts
$ pip install -r requirements.txt
$ python setup.py install

3、快速入门

3.1 生成 HTML（本地环境）

例子1：app.py

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

# V1 版本开始支持链式调用
bar = (
    Bar()
    .add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
    .add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
    .add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
    .set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况（爱看书的小沐）"))
)
bar.render()

# 不习惯链式调用的开发者依旧可以单独调用方法
bar = Bar()
bar.add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
bar.add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
bar.add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
bar.set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况（爱看书的小沐）"))
bar.render()

例子2：app2.py

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="Bar3D-基本示例 (爱看书的小沐)"),
    )
    .render("bar3d_base.html")
)

3.2 生成图片（本地环境）

例子1：app.py

from snapshot_selenium import snapshot as driver

from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.render import make_snapshot


def bar_chart() -> Bar:
    c = (
        Bar()
        .add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
        .add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
        .add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
        .reversal_axis()
        .set_series_opts(label_opts=opts.LabelOpts(position="right"))
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-测试渲染图片（爱看书的小沐）"))
    )
    return c

# 需要安装 snapshot-selenium 或者 snapshot-phantomjs
make_snapshot(driver, bar_chart().render(), "bar.png")

例子2：app2.py

from snapshot_selenium import snapshot as driver
from pyecharts.render import make_snapshot
from pyecharts import options as opts
from pyecharts.charts import Bar3D
import random

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

def bar3d_chart() -> Bar3D:
    
    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("Bar3D-堆叠柱状图示例(爱看书的小沐)"))
    bar3d.set_series_opts(**{"stack": "stack"})
    # bar3d.render("bar3d_stack.html")
    return bar3d

# 需要安装 snapshot-selenium 或者 snapshot-phantomjs
make_snapshot(driver, bar3d_chart().render(), "bar3d.png")

3.3 Jupyter Notebook（Notebook 环境）

https://jupyter.org/try-jupyter/retro/notebooks/?path=notebooks/Intro.ipynb https://jupyter.org/install

什么是Jupyter Notebook？ ① Jupyter Notebook是基于网页的用于交互计算的应用程序。其可被应用于全过程计算：开发、文档编写、运行代码和展示结果。 ② Jupyter Notebook是以网页的形式打开，可以在网页页面中直接编写代码和运行代码，代码的运行结果也会直接在代码块下显示的程序。如在编程过程中需要编写说明文档，可在同一个页面中直接编写，便于作及时的说明和解释。
Jupyter Notebook的主要特点 ① 编程时具有语法高亮、缩进、tab补全的功能。 ② 可直接通过浏览器运行代码，同时在代码块下方展示运行结果。 ③ 以富媒体格式展示计算结果。富媒体格式包括：HTML，LaTeX，PNG，SVG等。 ④ 对代码编写说明文档或语句时，支持Markdown语法。 ⑤ 支持使用LaTeX编写数学性说明。 Install the classic Jupyter Notebook with:

pip install notebook
jupyter notebook

# How do I open a specific Notebook?
jupyter notebook notebook.ipynb

# How do I start the Notebook using a custom IP or port?
jupyter notebook --port 9999

# How do I start the Notebook server without opening a browser?
jupyter notebook --no-browser

# How do I get help about Notebook server options?
jupyter notebook --help

# Running a notebook is this easy.
jupyter run notebook.ipynb

# You can pass more than one notebook as well.
jupyter run notebook.ipynb notebook2.ipynb

# By default, notebook errors will be raised and printed into the terminal. You can suppress them by passing the --allow-errors flag.
jupyter run notebook.ipynb --allow-errors

编辑代码：预览成果：

3.4 JupyterLab（Notebook 环境）

Install JupyterLab with pip:

pip install jupyterlab

Once installed, launch JupyterLab with:

jupyter-lab

鼠标点击NoteBook按钮，进入编辑界面，并输入代码如下如下：

3.5 Voilà（Notebook 环境）

# Install Voilà with:
pip install voila

# Once installed, launch Voilà with:
voila

浏览器访问：http://localhost:8866/ 查看某个ipynb文件如下：

4、地图 Map

4.1 Map_base

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

c = (
    Map()
    .add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
    .set_global_opts(title_opts=opts.TitleOpts(title="Map-基本示例"))
    .render("map_base.html")
)

4.2 Map_guangdong

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

c = (
    Map()
    .add("商家A", [list(z) for z in zip(Faker.guangdong_city, Faker.values())], "广东")
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Map-广东地图"), visualmap_opts=opts.VisualMapOpts()
    )
    .render("map_guangdong.html")
)

4.3 Map_visualmap_piecewise

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

c = (
    Map()
    .add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Map-VisualMap（分段型）"),
        visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True),
    )
    .render("map_visualmap_piecewise.html")
)

4.4 Map_world

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

c = (
    Map()
    .add("商家A", [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="Map-世界地图"),
        visualmap_opts=opts.VisualMapOpts(max_=200),
    )
    .render("map_world.html")
)

4.5 Population_density_of_hongkong_v2

import ssl

import pyecharts.options as opts
from pyecharts.charts import Map
from pyecharts.datasets import register_url

"""
Gallery 使用 pyecharts 1.1.0 和 echarts-china-cities-js
参考地址: https://echarts.apache.org/examples/editor.html?c=map-HK
"""
ssl._create_default_https_context = ssl._create_unverified_context
# 与 pyecharts 注册，当画香港地图的时候，用 echarts-china-cities-js
register_url("https://echarts-maps.github.io/echarts-china-cities-js")

WIKI_LINK = (
    "http://zh.wikipedia.org/wiki/"
    "%E9%A6%99%E6%B8%AF%E8%A1%8C%E6%94%BF%E5%8D%80%E5%8A%83#cite_note-12"
)
MAP_DATA = [
    ["中西区", 20057.34],
    ["湾仔", 15477.48],
    ["东区", 31686.1],
    ["南区", 6992.6],
    ["油尖旺", 44045.49],
    ["深水埗", 40689.64],
    ["九龙城", 37659.78],
    ["黄大仙", 45180.97],
    ["观塘", 55204.26],
    ["葵青", 21900.9],
    ["荃湾", 4918.26],
    ["屯门", 5881.84],
    ["元朗", 4178.01],
    ["北区", 2227.92],
    ["大埔", 2180.98],
    ["沙田", 9172.94],
    ["西贡", 3368],
    ["离岛", 806.98],
]


NAME_MAP_DATA = {
    # "key": "value"
    # "name on the hong kong map": "name in the MAP DATA",
    "中西区": "中西区",
    "东区": "东区",
    "离岛区": "离岛",
    "九龙城区": "九龙城",
    "葵青区": "葵青",
    "观塘区": "观塘",
    "北区": "北区",
    "西贡区": "西贡",
    "沙田区": "沙田",
    "深水埗区": "深水埗",
    "南区": "南区",
    "大埔区": "大埔",
    "荃湾区": "荃湾",
    "屯门区": "屯门",
    "湾仔区": "湾仔",
    "黄大仙区": "黄大仙",
    "油尖旺区": "油尖旺",
    "元朗区": "元朗",
}

(
    Map(init_opts=opts.InitOpts(width="1400px", height="800px"))
    .add(
        series_name="香港18区人口密度",
        maptype="香港",
        data_pair=MAP_DATA,
        name_map=NAME_MAP_DATA,
        is_map_symbol_show=False,
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="香港18区人口密度 （2011）",
            subtitle="人口密度数据来自Wikipedia",
            subtitle_link=WIKI_LINK,
        ),
        tooltip_opts=opts.TooltipOpts(
            trigger="item", formatter="{b}<br/>{c} (p / km2)"
        ),
        visualmap_opts=opts.VisualMapOpts(
            min_=800,
            max_=50000,
            range_text=["High", "Low"],
            is_calculable=True,
            range_color=["lightskyblue", "yellow", "orangered"],
        ),
    )
    .render("population_density_of_HongKong_v2.html")
)

4.6 Population_density_of_hongkong


import asyncio
from aiohttp import TCPConnector, ClientSession

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

"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://echarts.apache.org/examples/editor.html?c=map-HK
"""

WIKI_LINK = (
    "http://zh.wikipedia.org/wiki/"
    "%E9%A6%99%E6%B8%AF%E8%A1%8C%E6%94%BF%E5%8D%80%E5%8A%83#cite_note-12"
)

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()

# 下载香港地图
# data = asyncio.run(
#     get_json_data(url="https://echarts.apache.org/examples/data/asset/geo/HK.json")
# )

loop = asyncio.get_event_loop()
data = loop.run_until_complete(get_json_data(url="https://echarts.apache.org/examples/data/asset/geo/HK.json"))

MAP_DATA = [
    ["中西区", 20057.34],
    ["湾仔", 15477.48],
    ["东区", 31686.1],
    ["南区", 6992.6],
    ["油尖旺", 44045.49],
    ["深水埗", 40689.64],
    ["九龙城", 37659.78],
    ["黄大仙", 45180.97],
    ["观塘", 55204.26],
    ["葵青", 21900.9],
    ["荃湾", 4918.26],
    ["屯门", 5881.84],
    ["元朗", 4178.01],
    ["北区", 2227.92],
    ["大埔", 2180.98],
    ["沙田", 9172.94],
    ["西贡", 3368],
    ["离岛", 806.98],
]


NAME_MAP_DATA = {
    # "key": "value"
    # "name on the hong kong map": "name in the MAP DATA",
    "Central and Western": "中西区",
    "Eastern": "东区",
    "Islands": "离岛",
    "Kowloon City": "九龙城",
    "Kwai Tsing": "葵青",
    "Kwun Tong": "观塘",
    "North": "北区",
    "Sai Kung": "西贡",
    "Sha Tin": "沙田",
    "Sham Shui Po": "深水埗",
    "Southern": "南区",
    "Tai Po": "大埔",
    "Tsuen Wan": "荃湾",
    "Tuen Mun": "屯门",
    "Wan Chai": "湾仔",
    "Wong Tai Sin": "黄大仙",
    "Yau Tsim Mong": "油尖旺",
    "Yuen Long": "元朗",
}

(
    Map(init_opts=opts.InitOpts(width="1400px", height="800px"))
    .add_js_funcs("echarts.registerMap('HK', {});".format(data))
    .add(
        series_name="香港18区人口密度",
        maptype="HK",
        data_pair=MAP_DATA,
        name_map=NAME_MAP_DATA,
        is_map_symbol_show=False,
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="香港18区人口密度 （2011）",
            subtitle="人口密度数据来自Wikipedia",
            subtitle_link=WIKI_LINK,
        ),
        tooltip_opts=opts.TooltipOpts(
            trigger="item", formatter="{b}<br/>{c} (p / km2)"
        ),
        visualmap_opts=opts.VisualMapOpts(
            min_=800,
            max_=50000,
            range_text=["High", "Low"],
            is_calculable=True,
            range_color=["lightskyblue", "yellow", "orangered"],
        ),
    )
    .render("population_density_of_HongKong.html")
)

4.7 Map_china_citites

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

c = (
    Map()
    .add(
        "商家A",
        [list(z) for z in zip(Faker.guangdong_city, Faker.values())],
        "china-cities",
        label_opts=opts.LabelOpts(is_show=False),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Map-中国地图（带城市）"),
        visualmap_opts=opts.VisualMapOpts(),
    )
    .render("map_china_cities.html")
)

4.8 Map_without_label

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

c = (
    Map()
    .add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(title_opts=opts.TitleOpts(title="Map-不显示Label"))
    .render("map_without_label.html")
)

4.9 Map_visualmap

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

c = (
    Map()
    .add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Map-VisualMap（连续型）"),
        visualmap_opts=opts.VisualMapOpts(max_=200),
    )
    .render("map_visualmap.html")
)

4.10 Map3d_china_base

from pyecharts import options as opts
from pyecharts.charts import Map3D
from pyecharts.globals import ChartType

c = (
    Map3D()
    .add_schema(
        itemstyle_opts=opts.ItemStyleOpts(
            color="rgb(5,101,123)",
            opacity=1,
            border_width=0.8,
            border_color="rgb(62,215,213)",
        ),
        map3d_label=opts.Map3DLabelOpts(
            is_show=True,
            text_style=opts.TextStyleOpts(
                color="#fff", font_size=16, background_color="rgba(0,0,0,0)"
            ),
        ),
        emphasis_label_opts=opts.LabelOpts(is_show=True),
        light_opts=opts.Map3DLightOpts(
            main_color="#fff",
            main_intensity=1.2,
            is_main_shadow=False,
            main_alpha=55,
            main_beta=10,
            ambient_intensity=0.3,
        ),
    )
    .add(series_name="", data_pair="", maptype=ChartType.MAP3D)
    .set_global_opts(
        title_opts=opts.TitleOpts(title="全国行政区划地图-Base"),
        visualmap_opts=opts.VisualMapOpts(is_show=False),
        tooltip_opts=opts.TooltipOpts(is_show=True),
    )
    .render("map3d_china_base.html")
)

4.11 Map_globe_base

import pyecharts.options as opts
from pyecharts.charts import MapGlobe
from pyecharts.faker import POPULATION

data = [x for _, x in POPULATION[1:]]
low, high = min(data), max(data)

c = (
    MapGlobe()
    .add_schema()
    .add(
        maptype="world",
        series_name="World Population",
        data_pair=POPULATION[1:],
        is_map_symbol_show=False,
        label_opts=opts.LabelOpts(is_show=False),
    )
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(
            min_=low,
            max_=high,
            range_text=["max", "min"],
            is_calculable=True,
            range_color=["lightskyblue", "yellow", "orangered"],
        )
    )
    .render("map_globe_base.html")
)

结语

如果您觉得该方法或代码有一点点用处，可以给作者点个赞，或打赏杯咖啡；╮(￣▽￣)╭ 如果您感觉方法或代码不咋地//(ㄒoㄒ)//，就在评论处留言，作者继续改进；o_O??? 如果您需要相关功能的代码定制化开发，可以留言私信作者；(◡‿◡) 感谢各位大佬童鞋们的支持！( ´ ▽´ )ﾉ ( ´ ▽´)っ！！！

”侬今葬花人笑痴，他年葬侬知是谁？”

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【Web开发】Python实现Web图表功能（pyecharts入门学习）

1、简介

2、下载和安装

2.1 pip 安装

2.2 源码安装

3、快速入门

3.1 生成 HTML（本地环境）

3.2 生成图片（本地环境）

3.3 Jupyter Notebook（Notebook 环境）

3.4 JupyterLab（Notebook 环境）

3.5 Voilà（Notebook 环境）

4、地图 Map

4.1 Map_base

4.2 Map_guangdong

4.3 Map_visualmap_piecewise

4.4 Map_world

4.5 Population_density_of_hongkong_v2

4.6 Population_density_of_hongkong

4.7 Map_china_citites

4.8 Map_without_label

4.9 Map_visualmap

4.10 Map3d_china_base

4.11 Map_globe_base

结语

关于作者

on the way注册用户

【Web开发】Python实现Web图表功能（pyecharts入门学习）

1、简介

2、下载和安装

2.1 pip 安装

2.2 源码安装

3、快速入门

3.1 生成 HTML（本地环境）

3.2 生成图片（本地环境）

3.3 Jupyter Notebook（Notebook 环境）

3.4 JupyterLab（Notebook 环境）

3.5 Voilà（Notebook 环境）

4、地图 Map

4.1 Map_base

4.2 Map_guangdong

4.3 Map_visualmap_piecewise

4.4 Map_world

4.5 Population_density_of_hongkong_v2

4.6 Population_density_of_hongkong

4.7 Map_china_citites

4.8 Map_without_label

4.9 Map_visualmap

4.10 Map3d_china_base

4.11 Map_globe_base

结语

关于作者

on the way注册用户

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