matplotlib庫的數據可視化實驗心得體會詳情 - pyecharts繪圖,數據可視化,matplotlib擴展、,數據分析,html,數據可視化,人工智能誤會一場博客

pyecharts介紹

一. 簡介

pyecharts 是一個用於生成 Echarts 圖表的類庫。Echarts是百度開源的一個數據可視化 JS 庫。用 Echarts 生成的圖可視化效果非常棒，pyecharts 是為了與 Python 進行對接，方便在 Python 中直接使用數據生成圖。
官網：http://pyecharts.org/#/zh-cn/quickstart
Github：https://github.com/pyecharts/pyecharts/

二. 特性

簡潔的 API 設計，使用如絲滑般流暢，支持鏈式調用
囊括了 30+ 種常見圖表，應有盡有
支持主流 Notebook 環境，Jupyter Notebook 和 JupyterLab
可輕鬆集成至 Flask，Django 等主流 Web 框架
高度靈活的配置項，可輕鬆搭配出精美的圖表
詳細的文檔和示例，幫助開發者更快的上手項目
多達 400+ 地圖文件以及原生的百度地圖，為地理數據可視化提供強有力的支持

三. 版本

v0.5.x 和 V1 間不兼容，V1 是一個全新的版本，詳見
https://github.com/pyecharts/pyecharts/issues/892https://github.com/pyecharts/pyecharts/issues/1033
V0.5.x，支持 Python2.7，3.4+
V1 僅支持 Python3.6+
新版本系列將從 v1.0.0 開始，文檔位於 pyecharts.org

四. 安裝

安裝 v1 以上版本

pip/pip3 install pyecharts -U

如果需要安裝 0.5.11 版本的開發者，可以使用

pip/pip3 install pyecharts==0.5.11

matplotlib庫的數據可視化實驗心得體會_html

五. 快速開始

from pyecharts.charts import Bar
bar = Bar()
bar.add_xaxis(["襯衫", "羊毛衫", "雪紡衫", "褲子", "高跟鞋", "襪子"])
bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
# render 會生成本地 HTML 文件，默認會在當前目錄生成 render.html 文件
# 也可以傳入路徑參數，如 bar.render("mycharts.html")
bar.render()

matplotlib庫的數據可視化實驗心得體會_html_02

pyecharts 所有方法均支持鏈式調用

from pyecharts.charts import Bar
bar = (
    Bar()
    .add_xaxis(["襯衫", "羊毛衫", "雪紡衫", "褲子", "高跟鞋", "襪子"])
    .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
)
bar.render()

使用 options 配置項，在 pyecharts 中，一切皆 Options

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

# V1 版本開始支持鏈式調用
# 你所看到的格式其實是 `black` 格式化以後的效果
# 可以執行 `pip install black` 下載使用
bar = (
    Bar()
    .add_xaxis(["襯衫", "羊毛衫", "雪紡衫", "褲子", "高跟鞋", "襪子"])
    .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
    .set_global_opts(title_opts=opts.TitleOpts(title="主標題", subtitle="副標題"))
    # 或者直接使用字典參數
    # .set_global_opts(title_opts={"text": "主標題", "subtext": "副標題"})
)
bar.render()

# 不習慣鏈式調用的開發者依舊可以單獨調用方法
bar = Bar()
bar.add_xaxis(["襯衫", "羊毛衫", "雪紡衫", "褲子", "高跟鞋", "襪子"])
bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
bar.set_global_opts(title_opts=opts.TitleOpts(title="主標題", subtitle="副標題"))
bar.render()

matplotlib庫的數據可視化實驗心得體會_pyecharts繪圖_03

使用pyecharts繪製常見圖表

1. 柱狀圖

Bar-基本示例

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

def bar_base() -> Bar:
    c = (
        Bar()
        .add_xaxis(["襯衫", "羊毛衫", "雪紡衫", "褲子", "高跟鞋", "襪子"])
        .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
        .add_yaxis("商家B", [10, 25, 8, 60, 20, 80])
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-基本示例", subtitle="我是副標題"))
    )
    return c.render("0.html")
bar_base()

matplotlib庫的數據可視化實驗心得體會_matplotlib擴展、_04

Bar-漸變圓柱

def bar_border_radius():
    c = (
        Bar()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家A", Faker.values(), category_gap="60%")
        .set_series_opts(itemstyle_opts={
            "normal": {
                "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
                    offset: 0,
                    color: 'rgba(0, 244, 255, 1)'
                }, {
                    offset: 1,
                    color: 'rgba(0, 77, 167, 1)'
                }], false)"""),
                "barBorderRadius": [30, 30, 30, 30],
                "shadowColor": 'rgb(0, 160, 221)',
            }})
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-漸變圓柱"))
    )
    return c.render("1.html")
bar_border_radius()

matplotlib庫的數據可視化實驗心得體會_pyecharts繪圖_05

Bar-顯示 ToolBox

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


def bar_toolbox() -> Bar:
    c = (
        Bar()
        .add_xaxis(["襯衫","羊毛衫","雪紡衫","褲子","高跟鞋","襪子"])
        .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
        .add_yaxis("商家B", [10, 25, 8, 60, 20, 80])
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Bar-顯示 ToolBox"),
            toolbox_opts=opts.ToolboxOpts(),
            legend_opts=opts.LegendOpts(is_show=False)
        )
    )
    return c.render("2.html")
bar_toolbox()

matplotlib庫的數據可視化實驗心得體會_pyecharts繪圖_06

Bar-翻轉 XY 軸

def bar_reversal_axis() -> Bar:
    c = (
        Bar()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家A", Faker.values())
        .add_yaxis("商家B", Faker.values())
        .reversal_axis()
        .set_series_opts(label_opts=opts.LabelOpts(position="right"))
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-翻轉 XY 軸"))
    )
    return c.rebder("3.html")

matplotlib庫的數據可視化實驗心得體會_html_07

Bar-堆疊數據（全部）

def bar_stack0() -> Bar:
    c = (
        Bar()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家A", Faker.values(), stack="stack1")
        .add_yaxis("商家B", Faker.values(), stack="stack1")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-堆疊數據（全部）"))
    )
    return c.render("4.html")

matplotlib庫的數據可視化實驗心得體會_pyecharts繪圖_08

Bar-MarkPoint（指定類型）

def bar_markpoint_type() -> Bar:
    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-MarkPoint（指定類型）"))
        .set_series_opts(
            label_opts=opts.LabelOpts(is_show=False),
            markpoint_opts=opts.MarkPointOpts(
                data=[
                    opts.MarkPointItem(type_="max", name="最大值"),
                    opts.MarkPointItem(type_="min", name="最小值"),
                    opts.MarkPointItem(type_="average", name="平均值"),
                ]
            ),
        )
    )
    return c.render"5.html")

matplotlib庫的數據可視化實驗心得體會_matplotlib擴展、_09

2. 熱力圖

import random
from pyecharts.faker import Faker
from pyecharts import options as opts
from pyecharts.charts import HeatMap


def heatmap_base() -> HeatMap:
    value = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
    c = (
        HeatMap()
        .add_xaxis(Faker.clock)
        .add_yaxis("series0", Faker.week, value)
        .set_global_opts(
            title_opts=opts.TitleOpts(title="HeatMap-基本示例"),
            visualmap_opts=opts.VisualMapOpts(),
        )
    )
    return c.render("6.html")
heatmap_base()

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3. 折線圖

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


def line_base() -> Line:
    c = (
        Line()
        .add_xaxis(["襯衫", "羊毛衫", "雪紡衫", "褲子", "高跟鞋", "襪子"])
        .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
        .add_yaxis("商家B", [10, 25, 8, 60, 20, 80])
        .set_global_opts(title_opts=opts.TitleOpts(title="Line-基本示例"))
    )
    return c.render("7.html")
line_base()

matplotlib庫的數據可視化實驗心得體會_html_11

4. 面積圖

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


def line_areastyle() -> Line:
    c = (
        Line()
        .add_xaxis(["襯衫", "羊毛衫", "雪紡衫", "褲子", "高跟鞋", "襪子"])
        .add_yaxis(
            "商家A", [5, 20, 36, 10, 75, 90], areastyle_opts=opts.AreaStyleOpts(opacity=0.5)
        )
        .add_yaxis(
            "商家B", [10, 25, 8, 60, 20, 80], areastyle_opts=opts.AreaStyleOpts(opacity=0.5)
        )
        .set_global_opts(title_opts=opts.TitleOpts(title="Line-面積圖"))
    )
    return c.render("8.html")
line_areastyle()

matplotlib庫的數據可視化實驗心得體會_html_12

5. 散點圖

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


def scatter_base() -> Scatter:
    c = (
        Scatter()
        .add_xaxis(["襯衫", "羊毛衫", "雪紡衫", "褲子", "高跟鞋", "襪子"])
        .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
        .set_global_opts(title_opts=opts.TitleOpts(title="Scatter-基本示例"))
    )
    return c.render("9.html")


scatter_base()

matplotlib庫的數據可視化實驗心得體會_matplotlib擴展、_13

6. 層疊多圖

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


def overlap_line_scatter() -> Bar:
    x = Faker.choose()
    bar = (
        Bar()
        .add_xaxis(x)
        .add_yaxis("商家A", Faker.values())
        .add_yaxis("商家B", Faker.values())
        .set_global_opts(title_opts=opts.TitleOpts(title="Overlap-line+scatter"))
    )
    line = (
        Line()
        .add_xaxis(x)
        .add_yaxis("商家A", Faker.values())
        .add_yaxis("商家B", Faker.values())
    )
    bar.overlap(line)
    return bar

overlap_line_scatter().render("10.html")

matplotlib庫的數據可視化實驗心得體會_html_14

7. 日曆圖

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


def calendar_base() -> Calendar:
    begin = datetime.date(2019, 1, 1)
    end = datetime.date(2019, 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_="2019"))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Calendar-2019年微信步數情況"),
            visualmap_opts=opts.VisualMapOpts(
                max_=20000,
                min_=500,
                orient="horizontal",
                is_piecewise=True,
                pos_top="230px",
                pos_left="100px",
            ),
        )
    )
    return c.render("11.html")
calendar_base()

matplotlib庫的數據可視化實驗心得體會_pyecharts繪圖_15

8. 儀表盤

from pyecharts import options as opts
from pyecharts.charts import Gauge, Page


def gauge_base() -> Gauge:
    c = (
        Gauge()
        .add("", [("完成率", 66.6)])
        .set_global_opts(title_opts=opts.TitleOpts(title="Gauge-基本示例"))
    )
    return c.render("12.html")

matplotlib庫的數據可視化實驗心得體會_數據可視化_16

9.餅圖

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


def pie_base() -> Pie:
    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}"))
    )
    return c.render("13.html")

matplotlib庫的數據可視化實驗心得體會_數據可視化_17

10. 世界地圖

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


def map_world() -> Map:
    value = [95.1, 23.2, 43.3, 66.4, 88.5]
    attr = ["China", "Canada", "Brazil", "Russia", "United States"]
    c = (
        Map()
        .add("商家A", [list(z) for z in zip(attr, value)], "world")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Map-世界地圖"),
            visualmap_opts=opts.VisualMapOpts(max_=200),
        )
    )
    return c.render("13.html")

map_world()

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


def map_world() -> Map:
    value = [95.1, 23.2, 43.3, 66.4, 88.5]
    attr = ["雲南", "四川", "山西", "上海", "湖南"]
    c = (
        Map()
        .add("商家A", [list(z) for z in zip(attr, value)], "china")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Map-中國地圖"),
            visualmap_opts=opts.VisualMapOpts(max_=200),
        )
    )
    return c.render("14.html")

map_world()

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


def map_world() -> Map:
    value = [95.1, 23.2, 43.3, 66.4, 88.5]
    attr = ["昆明市", "曲靖市", "麗江市", "普洱市", "大理白族自治州"]
    c = (
        Map()
        .add("商家A", [list(z) for z in zip(attr, value)], "雲南")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Map-雲南地圖"),
            visualmap_opts=opts.VisualMapOpts(max_=200),
        )
    )
    return c.render("15.html")

map_world()

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