Web开发Python实现Web图表功能(pyecharts入门学习)
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<font color=purple face=华文行楷 size="5">"柳丝榆荚自芳菲,不管桃飘与李飞;"
1、简介
-
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
<font color=blue> 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;
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