python pyecharts数据可视化库

首先需要在激活anaconda环境,并在anaconda的python环境中安装pyecharts数据可视化库。

pip install pyecharts

pyecharts官网:pyecharts - A Python Echarts Plotting Library built with love.

pyecharts示例代码:Document

注意将.render("xxx.html")注释,改成在最后调用c.render_notebook(),这样才能在jupyter中显示。

from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.globals import ThemeType

list2 = [
    {"value": 12, "percent": 12 / (12 + 3)},
    {"value": 23, "percent": 23 / (23 + 21)},
    {"value": 33, "percent": 33 / (33 + 5)},
    {"value": 3, "percent": 3 / (3 + 52)},
    {"value": 33, "percent": 33 / (33 + 43)},
]

list3 = [
    {"value": 3, "percent": 3 / (12 + 3)},
    {"value": 21, "percent": 21 / (23 + 21)},
    {"value": 5, "percent": 5 / (33 + 5)},
    {"value": 52, "percent": 52 / (3 + 52)},
    {"value": 43, "percent": 43 / (33 + 43)},
]

c = (
    Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
    .add_xaxis([1, 2, 3, 4, 5])
    .add_yaxis("product1", list2, stack="stack1", category_gap="50%")
    .add_yaxis("product2", list3, stack="stack1", category_gap="50%")
    .set_series_opts(
        label_opts=opts.LabelOpts(
            position="right",
            formatter=JsCode(
                "function(x){return Number(x.data.percent * 100).toFixed() + '%';}"
            ),
        )
    )
    
)
c.render_notebook()

python pyecharts数据可视化库_第1张图片

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


c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values())
    .add_yaxis("商家B", Faker.values())
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-基本示例", subtitle="我是副标题"))
    #.render("bar_base.html")
)
c.render_notebook()

 python pyecharts数据可视化库_第2张图片

 

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())])
    .set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本示例"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    #.render("pie_base.html")
)
c.render_notebook()

python pyecharts数据可视化库_第3张图片

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

c = (
    Line()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values())
    .add_yaxis("商家B", Faker.values())
    .set_global_opts(title_opts=opts.TitleOpts(title="Line-基本示例"))
    #.render("line_base.html")
)
c.render_notebook()

python pyecharts数据可视化库_第4张图片

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


data = [
    {
        "children": [
            {"name": "B"},
            {
                "children": [{"children": [{"name": "I"}], "name": "E"}, {"name": "F"}],
                "name": "C",
            },
            {
                "children": [
                    {"children": [{"name": "J"}, {"name": "K"}], "name": "G"},
                    {"name": "H"},
                ],
                "name": "D",
            },
        ],
        "name": "A",
    }
]
c = (
    Tree()
    .add("", data)
    .set_global_opts(title_opts=opts.TitleOpts(title="Tree-基本示例"))
    #.render("tree_base.html")
)
c.render_notebook()

python pyecharts数据可视化库_第5张图片

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

c = (
    Scatter()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values())
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Scatter-显示分割线"),
        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
        yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
    )
    #.render("scatter_splitline.html")
)
c.render_notebook()

 python pyecharts数据可视化库_第6张图片

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

nodes = [
    {"name": "结点1", "symbolSize": 10},
    {"name": "结点2", "symbolSize": 20},
    {"name": "结点3", "symbolSize": 30},
    {"name": "结点4", "symbolSize": 40},
    {"name": "结点5", "symbolSize": 50},
    {"name": "结点6", "symbolSize": 40},
    {"name": "结点7", "symbolSize": 30},
    {"name": "结点8", "symbolSize": 20},
]
links = []
for i in nodes:
    for j in nodes:
        links.append({"source": i.get("name"), "target": j.get("name")})
c = (
    Graph()
    .add("", nodes, links, repulsion=8000)
    .set_global_opts(title_opts=opts.TitleOpts(title="Graph-基本示例"))
    #render("graph_base.html")
)
c.render_notebook()

python pyecharts数据可视化库_第7张图片

 

import datetime
import random

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


begin = datetime.date(2017, 1, 1)
end = datetime.date(2017, 12, 31)
data = [
    [str(begin + datetime.timedelta(days=i)), random.randint(1000, 25000)]
    for i in range((end - begin).days + 1)
]

c = (
    Calendar()
    .add("", data, calendar_opts=opts.CalendarOpts(range_="2017"))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Calendar-2017年微信步数情况"),
        visualmap_opts=opts.VisualMapOpts(
            max_=20000,
            min_=500,
            orient="horizontal",
            is_piecewise=True,
            pos_top="230px",
            pos_left="100px",
        ),
    )
    #.render("calendar_base.html")
)
c.render_notebook()

 python pyecharts数据可视化库_第8张图片

 

 

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