Common usage of Python's pyecharts 3-polar coordinate diagram-funnel diagram

Pyecharts is a Python visualization library based on Echarts, which can easily generate various interactive charts and maps in Python language. It supports a variety of chart types, including line charts, histograms, scatter charts, pie charts, maps, etc., and can realize data visualization through simple API calls.

Advantages of Pyecharts include:

1. Easy to use: Pyecharts provides an easy-to-use API that can easily generate various charts and maps.

2. Rich chart types: Pyecharts supports a variety of chart types, including line charts, histograms, scatter charts, pie charts, maps, etc.

3. Interactive visualization: The charts generated by Pyecharts can be interactively operated, including zooming, dragging, data filtering, etc.

4. Support multiple data formats: Pyecharts supports multiple data formats, including CSV, JSON, Excel, etc.

5. Strong scalability: Pyecharts can be integrated with other Python libraries and frameworks, such as Pandas, Flask, Django, etc.

In short, Pyecharts is a powerful, easy-to-use Python visualization library that can help developers quickly generate various interactive charts and maps.

Today we will take a look at the common basic usage of Pyecharts, Lesson 3

1. Polar coordinate diagram - circular

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

c = (
    Polar()
    .add_schema(
        radiusaxis_opts=opts.RadiusAxisOpts(data=Faker.week, type_="category"),
        angleaxis_opts=opts.AngleAxisOpts(is_clockwise=True, max_=10),
    )
    .add("A", [1, 2, 3, 4, 3, 5, 1], type_="bar")
    .set_global_opts(title_opts=opts.TitleOpts(title="Polar-RadiusAxis"))
    .set_series_opts(label_opts=opts.LabelOpts(is_show=True))
    .render("polar_radius.html")
)

Polar plot - scatter

import random

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

data = [(i, random.randint(1, 100)) for i in range(101)]
c = (
    Polar()
    .add("", data, type_="scatter", label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(title_opts=opts.TitleOpts(title="Polar-Scatter0"))
    .render("polar_scatter_0.html")
)

Polar Plot - Angles

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

c = (
    Polar()
    .add_schema(angleaxis_opts=opts.AngleAxisOpts(data=Faker.week, type_="category"))
    .add("A", [1, 2, 3, 4, 3, 5, 1], type_="bar", stack="stack0")
    .add("B", [2, 4, 6, 1, 2, 3, 1], type_="bar", stack="stack0")
    .add("C", [1, 2, 3, 4, 1, 2, 5], type_="bar", stack="stack0")
    .set_global_opts(title_opts=opts.TitleOpts(title="Polar-AngleAxis"))
    .render("polar_angleaxis.html")
)

Polar plot - dynamic scatterplot

import random

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


data = [(i, random.randint(1, 100)) for i in range(10)]
c = (
    Polar()
    .add(
        "",
        data,
        type_="effectScatter",
        effect_opts=opts.EffectOpts(scale=10, period=5),
        label_opts=opts.LabelOpts(is_show=False),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="Polar-EffectScatter"))
    .render("polar_effectscatter.html")
)

2. Funnel diagram

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

c = (
    Funnel()
    .add(
        "商品",
        [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")
)

2. Funnel Chart-1

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

c = (
    Funnel()
    .add("商品", [list(z) for z in zip(Faker.choose(), Faker.values())])
    .set_global_opts(title_opts=opts.TitleOpts(title="Funnel-基本示例"))
    .render("funnel_base.html")
)

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