Pyecharts绘制22种超实用精美图表

短命女 2022-08-28 10:43 202阅读 0赞

今天来给大家分享一下Pyecharts模块，说到它我们就不得不提Echarts，它是由百度开源的一款使用JavaScript实现的开源可视化库，涵盖了各种图表、满足各类业务需求，**而pyecharts也就是Python与Echarts结合之后的产物**，封装了Echarts各类图表的基本操作，然后通过渲染机制，输出一个包含JS代码的HTML文件。

![3ee8116bedeeb49283a6538fcb61790c.png][]

**0****1**

安装与导入模块

说到安装模块，我们可以这样来进行，

pip install pyecharts

使用Pyecharts创建图形的基本步骤是

1. 准备数据

2. 设计图形的样式、背景颜色

3. Pyecharts绘图

4. 设计图表的标题或者图例等属性

5. 导出至html

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1", Faker.values())
        .add_yaxis("商家2", Faker.values())
        .set_global_opts(title_opts=opts.TitleOpts(title="这是主标题", subtitle="这是副标题"))
        .render("bar_base.html")
    )

出来的结果是

![3a3db0029c2e7f5fb7937233acff18cf.png][]

**0****2**

数据准备

import pandas as pd
    import numpy as np
    
    
    data = pd.DataFrame({'x':np.arange(1,101),
                         'y':["随机生成的数字"]})
    
    
    df = pd.read_excel("你的文件的路径")

**0****3**

Pycharts还提供内置的数据集

Pyecharts内部还提供了一些数据集，主要包含**类别数据、时间数据、颜色数据**、地理数据、世界人口数据等等，通过choose()方法来随机选择使用哪个

def choose(self) -> list:
        return random.choice(
            [
                self.clothes,
                self.drinks,
                self.phones,
                self.fruits,
                self.animal,
                self.dogs,
                self.week,
            ]
        )

**04**

图形的样式

说到图形的样式，大概都这么几种

class _ThemeType:
        BUILTIN_THEMES = ["light", "dark", "white"]
        LIGHT = "light"
        DARK = "dark"
        WHITE = "white"
        CHALK: str = "chalk"
        ESSOS: str = "essos"
        INFOGRAPHIC: str = "infographic"
        MACARONS: str = "macarons"
        PURPLE_PASSION: str = "purple-passion"
        ROMA: str = "roma"
        ROMANTIC: str = "romantic"
        SHINE: str = "shine"
        VINTAGE: str = "vintage"
        WALDEN: str = "walden"
        WESTEROS: str = "westeros"
        WONDERLAND: str = "wonderland"
        HALLOWEEN: str = "halloween"

**0****6**

设置标题、副标题

设置标题以及副标题的代码如下

set_global_opts(title_opts=opts.TitleOpts(title="这是主标题",
                                              subtitle="这是副标题"))

**0****7**

设置图例与位置

legend_opts=opts.LegendOpts(type_="scroll", orient="vertical",
                                pos_top="15%",pos_left="7%")) # 图裂的位置
    label_opts=opts.LabelOpts(formatter="{b}: {c}") # 结果的展现形式

**0****8**

导出结果

render("test.html")
    
    
    # 如果是在jupyter notebook当中
    render_notebook()

**0****9**

Pyecharts绘图

堆叠柱状图

同个品类不同类目的柱子可以堆叠起来呈现，也就是堆叠的柱状图

c = (
        Bar()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家1", Faker.values(), stack="stack1")
        .add_yaxis("商家2", Faker.values(), stack="stack1")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-堆叠数据（全部）"))
        .render("bar_stack_1212.html")
    )

![71c7136bbb217988f083388da2696be8.png][]

当然我们也可以部分堆叠，而不是上面这种全部的堆叠

c = (
        Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
        .add_xaxis(Faker.choose())
        .add_yaxis("商家1", Faker.values(), stack="stack0")
        .add_yaxis("商家2", Faker.values(), stack="stack0")
        .add_yaxis("商家3", Faker.values())
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-堆叠数据（部分）"))
        .render("bar_stack_part.html")
    )

![cdf7c96084e6fd5573acf180b3d553a5.png][]

柱状图的横坐标倾斜一丢丢

有时候横坐标的标识字数较多，X轴上显示全，我们可以将标识的字体稍微倾斜一些

c = (
        Bar()
        .add_xaxis(
            [
                "名字相当长的X轴标签1",
                "名字相当长的X轴标签2",
                "名字相当长的X轴标签3",
                "名字相当长的X轴标签4",
                "名字相当长的X轴标签5",
                "名字相当长的X轴标签6",
            ]
        )
        .add_yaxis("商家1", Faker.values())
        .add_yaxis("商家2", Faker.values())
        .set_global_opts(
            xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=15)),
            title_opts=opts.TitleOpts(title="Bar-旋转X轴标签", subtitle="副标题"),
        )
        .render("test.html")
    )

![bf9740e84bfdd253a7e8e949a28bdf53.png][]

柱状图可以自动缩放的

通过底下的滑块来实现横坐标的缩放、范围的调整等等

c = (
        Bar()
        .add_xaxis(Faker.days_attrs)
        .add_yaxis("商家1", Faker.days_values)
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Bar-数据缩放（拖快-水平）"),
            datazoom_opts=opts.DataZoomOpts(),
        )
        .render("bar_datazoom_slider.html")
    )

![be7b5112ea337156fe73fe68c85fe02d.gif][]

当然滑块也可以放在垂直的右侧

c = (
        Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
        .add_xaxis(Faker.days_attrs)
        .add_yaxis("商家1", Faker.days_values, color=Faker.rand_color())
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Bar-DataZoom（滑块-垂直）"),
            datazoom_opts=opts.DataZoomOpts(orient="vertical"),
        )
        .render("bar_datazoom_slider_vertical.html")
    )

![9c68792c239c71da41883032435f5d10.gif][]

我们也可以通过拖动里面的柱子来实现数据缩放、范围的改变

c = (
        Bar()
        .add_xaxis(Faker.days_attrs)
        .add_yaxis("商家1", Faker.days_values)
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Bar-DataZoom（内置+外置）"),
            datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
        )
        .render("bar_datazoom_both.html")
    )

![23ed6abfdc696250d1ce9be1c3d82fed.gif][]

柱状图给X轴Y轴命名的

c = (
        Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
        .add_xaxis(Faker.choose())
        .add_yaxis("商家1", Faker.values())
        .add_yaxis("商家2", Faker.values())
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Bar-XY 轴名称"),
            yaxis_opts=opts.AxisOpts(name="这个是 Y 轴"),
            xaxis_opts=opts.AxisOpts(name="这个是 X 轴"),
        )
        .render("bar_name_xyaxis.html")
    )

![8caa7fb66905e7faaa670ff89560339d.png][]

柱状图柱间距离不相同的

在柱状图当中，不同柱子之间的距离也可以不是相同的

c = (
        Bar(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
        .add_xaxis(Faker.choose())
        .add_yaxis("商家1", Faker.values(), gap="0%")
        .add_yaxis("商家2", Faker.values(), gap="0%")
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-柱间距离不同"))
        .render("bar_different_series_gap.html")
    )

![af6f64020e49e8bbda236643d88e3010.png][]

柱状图水平状态的

还有水平方向的柱状图

c = (
        Bar()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家1", Faker.values())
        .add_yaxis("商家2", Faker.values())
        .reversal_axis()
        .set_series_opts(label_opts=opts.LabelOpts(position="right"))
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-水平方向"))
        .render("bar_reversal_axis.html")
    )

![a0201a08a8477a412ac3f1157cd77c98.png][]

直方图

c = (
        Bar()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家1", Faker.values(), category_gap=0, color=Faker.rand_color())
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-直方图"))
        .render("bar_histogram.html")
    )

![afd46355491c6d08274587053fdbb469.png][]

箱型图

箱型图更加有利于我们来观察数据的内在分布

from pyecharts.charts import Boxplot
    
    
    v1 = [
        [850, 740, 950, 1090, 930, 850, 950, 980, 1000, 880, 1000, 980],
        [980, 940, 960, 940, 900, 800, 850, 880, 950, 840, 830, 800],
    ]
    v2 = [
        [890, 820, 820, 820, 800, 770, 760, 760, 750, 760, 950, 920],
        [900, 840, 800, 810, 760, 810, 790, 850, 820, 850, 870, 880],
    ]
    c = Boxplot()
    c.add_xaxis(["A", "B"])
    c.add_yaxis("类目1", c.prepare_data(v1))
    c.add_yaxis("类目2", c.prepare_data(v2))
    c.set_global_opts(title_opts=opts.TitleOpts(title="箱型图-基本示例"))
    c.render("boxplot_test.html")

![35db5c65410b9a6c0b498888da0b05fe.png][]

日历图

日历图具体指按照日历的布局，**用颜色展现每一天的数据**，从而比较直观地看到**全年的数据情况**，例如展示超市全年的销售额，从而看出具体某个月份或者某个星期的销售额比较低

c = (
        Calendar(init_opts=opts.InitOpts(theme=ThemeType.INFOGRAPHIC))
        .add("", data, calendar_opts=opts.CalendarOpts(range_="2020"))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="日历图-2020年超市的销售额"),
            visualmap_opts=opts.VisualMapOpts(
                max_=250000,
                min_=10000,
                orient="horizontal",
                is_piecewise=True,
                pos_top="230px",
                pos_left="100px",
            ),
        )
        .render("calendar_test.html")
    )

![e13aeee3ea3bea2e398a3e40ff872a49.png][]

K线图

c = (
        Kline(init_opts=opts.InitOpts(theme=ThemeType.ESSOS))
        .add_xaxis(["2020/7/{}".format(i + 1) for i in range(31)])
        .add_yaxis("kline", data)
        .set_global_opts(
            yaxis_opts=opts.AxisOpts(is_scale=True),
            xaxis_opts=opts.AxisOpts(is_scale=True),
            title_opts=opts.TitleOpts(title="K线图-基本示例"),
        )
        .render("kline_test.html")
    )

![71f590800cd6b9ec6f6e54a903ef76d0.png][]

漏斗图

from pyecharts.charts import Funnel
    
    
    c = (
        Funnel()
        .add("类目", [list(z) for z in zip(Faker.choose(), Faker.values())])
        .set_global_opts(title_opts=opts.TitleOpts(title="漏斗图-基本示例"))
        .render("funnel_test.html")
    )

![e86b538c560b935dc9b0e644a947adf8.png][]

折线图

c = (
        Line()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家1", Faker.values())
        .add_yaxis("商家2", Faker.values())
        .set_global_opts(title_opts=opts.TitleOpts(title="折线图-基本示例"))
        .render("line_test.html")
    )

![f33f66cb377f4404b001a3677460d11a.png][]

水球图

水球图通常来显示指标的**完成程度**

from pyecharts.charts import Liquid
    
    
    c = (
        Liquid()
        .add("lq", [0.55, 0.75])
        .set_global_opts(title_opts=opts.TitleOpts(title="Liquid-基本示例"))
        .render("liquid_test.html")
    )

![77d479ca205afcc6934055ba2b1dacbe.gif][]

词云图

c = (
        WordCloud()
        .add(series_name="词云图实例", data_pair=data, word_size_range=[5, 100])
        .set_global_opts(
            title_opts=opts.TitleOpts(
                title="词云图实例", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
            ),
            tooltip_opts=opts.TooltipOpts(is_show=True),
        )
        .render("basic_wordcloud.html")
    )

![acb26700d64af62331eefca4f658f03c.png][]

饼图

c = (
        Pie()
        .add("类目", [list(z) for z in zip(Faker.choose(), Faker.values())])
        .set_global_opts(title_opts=opts.TitleOpts(title="饼图-基本示例"))
        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
        .render("pie_test.html")
    )

![ce6f34b34d21d993d9e0c0968381284a.png][]

仪表盘图

仪表盘的绘制也可以用来展示指标的完成程度

from pyecharts.charts import Gauge
    
    
    c = (
        Gauge()
        .add("", [("完成率", 70)])
        .set_global_opts(title_opts=opts.TitleOpts(title="仪表盘-基本示例"))
        .render("gauge_test.html")
    )

![f4e87245c3cdb1b580da7462acd33fa1.gif][]

地图

c = (
        Map()
        .add("商家1", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
        .set_global_opts(title_opts=opts.TitleOpts(title="地图-基本示例"))
        .render("map_test.html")
    )

![c65bf266d29f9789e70b1681d8b5010c.png][]

涟漪散点图

c = (
        EffectScatter()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家1", Faker.values())
        .set_global_opts(title_opts=opts.TitleOpts(title="涟漪散点图-基本示例"))
        .render("effectscatter_test.html")
    )

![74d12f617baeccef0960ef8fb0b37001.png][]

**HISTORY**

**往期推荐**

[年轻人如何才能实现年薪百万呢？用数据分析来帮你探探路][Link 1]

[分享几个用Python给图片添加水印的方法，简单实用][Python]

[介绍如何用Python来绘制高清的交互式地图，建议收藏][Python 1]

[国内最让人失望的景点有哪些？哪些景点冷门却又十分好玩、值得一去，数据来为你揭晓][Link 2]

分享、收藏、点赞、在看安排一下？

![01a0efd82f0f4a6a85cb04486a0e551c.gif][]

![93e65d2bf543a6e6bbe2d1ddae5febbf.gif][]

![35270f03f2f3179e365aae018c1c1359.gif][]

![324718b62246acea2a9ca160359180ab.gif][]

[3ee8116bedeeb49283a6538fcb61790c.png]: /images/20220828/e9341e60f59846279c80080e782191ab.png
[3a3db0029c2e7f5fb7937233acff18cf.png]: /images/20220828/3c6b894f06324d8ca488d853218a1b97.png
[71c7136bbb217988f083388da2696be8.png]: /images/20220828/8e8236daec754864a80db7225bcb8819.png
[cdf7c96084e6fd5573acf180b3d553a5.png]: /images/20220828/d491aa8b5d5f481f87e3b8afe9b874e1.png
[bf9740e84bfdd253a7e8e949a28bdf53.png]: /images/20220828/5bb6790d718b45fdbc5cb7164a422db2.png
[be7b5112ea337156fe73fe68c85fe02d.gif]: /images/20220828/24973e1137f542719d76e4659e550fc8.png
[9c68792c239c71da41883032435f5d10.gif]: /images/20220828/d80ed42098af4046b2f9aff01bb1e310.png
[23ed6abfdc696250d1ce9be1c3d82fed.gif]: /images/20220828/f02fb763f6b944e8b89f3deae86cc58f.png
[8caa7fb66905e7faaa670ff89560339d.png]: /images/20220828/117d7ed2363e4711939b96aae8f14a92.png
[af6f64020e49e8bbda236643d88e3010.png]: /images/20220828/077922e5cce3438e8fc38f8dc839c91c.png
[a0201a08a8477a412ac3f1157cd77c98.png]: /images/20220828/0f1b902831774c67b06ca250d16d729c.png
[afd46355491c6d08274587053fdbb469.png]: /images/20220828/77697a565d9a40f196dec1bbec800aff.png
[35db5c65410b9a6c0b498888da0b05fe.png]: /images/20220828/5a09be71b7834e8199f5421108407909.png
[e13aeee3ea3bea2e398a3e40ff872a49.png]: /images/20220828/5d42d9eb961248d387db32b61819efac.png
[71f590800cd6b9ec6f6e54a903ef76d0.png]: /images/20220828/d22f79affa4648e9b25a9beb772e84f3.png
[e86b538c560b935dc9b0e644a947adf8.png]: /images/20220828/0b415b6805634fdc9725668231cd9163.png
[f33f66cb377f4404b001a3677460d11a.png]: /images/20220828/edc50d494ae14677855272bf7b095924.png
[77d479ca205afcc6934055ba2b1dacbe.gif]: /images/20220828/d735b89ca9ff4bf58029034099f451c5.png
[acb26700d64af62331eefca4f658f03c.png]: /images/20220828/ff50867f1a214fef96884d8fd0105a79.png
[ce6f34b34d21d993d9e0c0968381284a.png]: /images/20220828/097e4962f99f4cf38f122f8e8dc9b362.png
[f4e87245c3cdb1b580da7462acd33fa1.gif]: /images/20220828/45dd503bec2c43619be63622dc63d735.png
[c65bf266d29f9789e70b1681d8b5010c.png]: /images/20220828/2b7785a456be40ea9a0806784c1d32ed.png
[74d12f617baeccef0960ef8fb0b37001.png]: /images/20220828/df598f36d65044b2b924f5bd3f9c548f.png
[Link 1]: https://blog.csdn.net/weixin_43373042/article/details/120520716
[Python]: https://blog.csdn.net/fengdu78/article/details/120500241
[Python 1]: https://blog.csdn.net/weixin_43373042/article/details/120446569
[Link 2]: https://blog.csdn.net/yuanziok/article/details/120431553
[01a0efd82f0f4a6a85cb04486a0e551c.gif]: /images/20220828/4c708e11d02047528fb5fa1e960eca5a.png
[93e65d2bf543a6e6bbe2d1ddae5febbf.gif]: /images/20220828/de99aec4a4da469b8281f08e8d74632f.png
[35270f03f2f3179e365aae018c1c1359.gif]: /images/20220828/5ed87897219a4897ac91ee7141cdc186.png
[324718b62246acea2a9ca160359180ab.gif]: /images/20220828/b199bd4745bf4554a6929be4545471ad.png