python数据可视化之pyecharts

DataGirl 2021-05-17

1285

Python ❤️ Echarts = pyecharts

pyecharts 是 Python 和 Echarts 的结合，绘制出的图表十分精美且种类丰富

pyecharts目前更新到 1.7.0，1.x版本与0.5.x版本在语法上有很大区别

安装

这里我们直接安装最新版本：

pip(3) install pyecharts

如果安装后导入有问题，可以尝试源码安装：

$ git clone https://github.com/pyecharts/pyecharts.git
$ cd pyecharts
$ pip install -r requirements.txt
$ python setup.py install
# 或者执行 python install.py

柱形图

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


x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
y1 = [5, 20, 36, 10, 75, 90]
y2 = [15, 6, 45, 20, 35, 66]


bar = (
    Bar()
    .add_xaxis(x)
    .add_yaxis("商家A", y1)
    .add_yaxis("商家B", y2)
    .set_global_opts(title_opts=opts.TitleOpts(title="主标题",
                                               subtitle="副标题"))
)
# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件
# 也可以传入路径参数，如 bar.render("mycharts.html")
bar.render()

在notebook中显示图表

pyecharts也支持Jupyter Notebook 和 JupyterLab，但是在notebook中渲染图表时要求不同：

Jupyter Notebook 用 bar.render_notebook()直接渲染

JupyterLab 则稍微复杂一些：

必须在引入 pyecharts.charts 等模块前声明Notebook 类型

from pyecharts.globals import CurrentConfig, NotebookType
CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB

第一次渲染时要先用 bar.load_javascript() 预先加载基本 JavaScript 文件到 Notebook 中
然后在不同的cell中运行bar.render_notebook()

这样就可以一边运行代码，一边查看图片结果啦。

Bar-DataZoom（slider+inside）

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




bar = (
        Bar()
        .add_xaxis(Faker.days_attrs)
        .add_yaxis("", Faker.days_values, color=Faker.rand_color())
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Bar-DataZoom"),
            datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
        )
    )


bar.render_notebook()

效果比较炫酷

堆叠柱形图

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


x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
y1 = [5, 20, 36, 10, 75, 90]
y2 = [15, 6, 45, 20, 35, 66]


#通过stack="stack1"设置堆叠
bar = (
    Bar()
    .add_xaxis(x)
    .add_yaxis("商家A", y1, stack="stack1")
    .add_yaxis("商家B", y2, stack="stack1")
    .set_global_opts(title_opts=opts.TitleOpts(title="主标题",
                                               subtitle="副标题"))
)


bar.render_notebook()

更换图表风格

以上图表都是pyecharts的默认主题，其实pyecharts内置10+种不同主题，可以自行选用。

from pyecharts.charts import Bar
from pyecharts import options as opts
#引入主题模块
from pyecharts.globals import ThemeType


x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
y1 = [5, 20, 36, 10, 75, 90]
y2 = [15, 6, 45, 20, 35, 66]


# init_opts=opts.InitOpts(theme=ThemeType.LIGHT) 选用LIGHT主题
# .reversal_axis()交换 XY 轴
# .set_series_opts(label_opts=opts.LabelOpts(position="right")) 指定数据显示位置为右侧
bar = (
    Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
    .add_xaxis(x)
    .add_yaxis("商家A", y1)
    .add_yaxis("商家B", y2)
    .reversal_axis()
    .set_series_opts(label_opts=opts.LabelOpts(position="right"))
    .set_global_opts(title_opts=opts.TitleOpts(title="主题为LIGHT",
                                               subtitle="副标题"))
)


bar.render_notebook()

添加标记点

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


x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
y1 = [5, 20, 36, 10, 75, 90]
y2 = [15, 6, 45, 20, 35, 66]




# 通过markpoint_opts添加标记点
bar = (
    Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))
    .add_xaxis(x)
    .add_yaxis("商家A", y1)
    .add_yaxis("商家B", y2)
    .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"),               
                        ]
            ),
        )
    .set_global_opts(title_opts=opts.TitleOpts(title="主题为DARK",
                                               subtitle="最大值、最小值标记点"))
)


bar.render_notebook()

饼图

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.globals import ThemeType


list = [['可乐', 76], ['雪碧', 49], ['橙汁', 115]]


bar = (
    Pie(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
    .add("", list)
    .set_global_opts(title_opts=opts.TitleOpts(title="饼图"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b} : {c}  {d}%"))
)
bar.render_notebook()

多饼图

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.commons.utils import JsCode


fn = """
    function(params) {
        if(params.name == '其他')
            return '\\n\\n\\n' + params.name + ' : ' + params.value + '%';
        return params.name + ' : ' + params.value + '%';
    }
    """




def new_label_opts():
        return opts.LabelOpts(formatter=JsCode(fn), position="center")


c = (
        Pie()
        .add(
            "",
            [list(z) for z in zip(["剧情", "其他"], [25, 75])],
            center=["20%", "30%"],
            radius=[60, 80],
            label_opts=new_label_opts(),
        )
        .add(
            "",
            [list(z) for z in zip(["奇幻", "其他"], [24, 76])],
            center=["55%", "30%"],
            radius=[60, 80],
            label_opts=new_label_opts(),
        )
        .add(
            "",
            [list(z) for z in zip(["爱情", "其他"], [14, 86])],
            center=["20%", "70%"],
            radius=[60, 80],
            label_opts=new_label_opts(),
        )
        .add(
            "",
            [list(z) for z in zip(["惊悚", "其他"], [11, 89])],
            center=["55%", "70%"],
            radius=[60, 80],
            label_opts=new_label_opts(),
        )
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Pie-多饼图示例"),
            legend_opts=opts.LegendOpts(
                type_="scroll", pos_top="20%", pos_left="80%", orient="vertical"
            ),
        )
    )


c.render_notebook()

多维散点图

from pyecharts import options as opts
from pyecharts.charts import Scatter
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType
from pyecharts.commons.utils import JsCode


Scatter = (
        Scatter(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
        .add_xaxis(Faker.choose())
        .add_yaxis(
            "商家A",
            [list(z) for z in zip(Faker.values(), Faker.choose())],
            label_opts=opts.LabelOpts(
                formatter=JsCode(
                    "function(params){return params.value[1] +' : '+ params.value[2];}"
                ),
                #position="inside"
            ),
        )
         .set_global_opts(
            title_opts=opts.TitleOpts(title="Scatter-多维度数据"),
            tooltip_opts=opts.TooltipOpts(
                formatter=JsCode(
                    "function (params) {return params.name + ' : ' + params.value[2];}"
                )
            ),
            visualmap_opts=opts.VisualMapOpts(
                type_="size", max_=150, min_=20, dimension=1
            ),
        )
    )


Scatter.render_notebook()

漏斗图

from pyecharts import options as opts
from pyecharts.charts import Funnel, Page
from pyecharts.globals import ThemeType


labels = ['浏览人数', '加购人数', '购买人数']
data = [100, 50, 30]


funnel = (
        Funnel(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
        .add("", [list(z) for z in zip(labels, data)],
            label_opts=opts.LabelOpts(position="inside"))
        .set_global_opts(title_opts=opts.TitleOpts(title="销售转化漏斗"))
    )
    
funnel.render_notebook()

词云

from pyecharts import options as opts
from pyecharts.charts import Page, WordCloud
from pyecharts.globals import SymbolType


words = [
    ("火锅", 1446),
    ("面包", 928),
    ("油条", 906),
    ("牛奶", 825),
    ("凉皮", 514),
    ("早餐", 486),
    ("吐司", 53),
    ("芝士", 163),
    ("酸奶", 86),
    ("奶茶", 17),
    ("蛋糕", 678),
    ("烧烤", 567),
    ("土豆", 437),
    ("巧克力", 422),
    ("甜甜圈", 353),
    ("苹果", 331)]


wordcloud = (
        WordCloud()
        .add(
            "",
            words,
            word_size_range=[20, 100],
            rotate_step = 90,#设置文字角度
            textstyle_opts=opts.TextStyleOpts(font_family="cursive"),
        )
        .set_global_opts(title_opts=opts.TitleOpts(title="WordCloud"))
    )


wordcloud.render_notebook()

中国疫情地图

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


#数据截至 2020/2/29 22:14 现存确诊
data = [['湖北', 34617], ['广东', 366], ['山东', 332], ['浙江', 188], ['四川', 184], 
        ['湖南', 170], ['黑龙江', 166], ['重庆', 148],['北京', 132], ['江西', 123],
        ['安徽', 116], ['江苏', 108], ['河南', 81], ['广西', 74], ['香港', 62], 
        ['福建', 53],['上海', 47], ['陕西', 37], ['贵州', 32], ['河北', 31], 
        ['台湾', 29], ['内蒙古', 26], ['辽宁', 25], ['天津', 24],['山西', 20], 
        ['吉林', 17], ['海南', 16], ['云南', 15], ['新疆', 11], ['甘肃', 7], 
        ['宁夏', 4], ['澳门', 2],
        ['青海', 0], ['西藏', 0]]


map = (
        Map()
        .add("现存确诊", data, "china")
        .set_global_opts(
            title_opts=opts.TitleOpts(title="现存确诊疫情地图"),
            visualmap_opts=opts.VisualMapOpts(max_=35000, is_piecewise=True,
            pieces=[
                {"min": 10000, "label": '>10000人', "color": "#6666CC"},
                {"min": 1000, "max": 10000, "label": '1001-10000人', "color": "#9999FF"},
                {"min": 500, "max": 999, "label": '999-1000人', "color": "#CCCCFF"},
                {"min": 100, "max": 499, "label": '100-499人', "color": "#FF9999"},
                {"min": 10, "max": 99, "label": '10-99人', "color": "#FFCCCC"},
                {"min": 1, "max": 9, "label": '0-9人', "color": "#CCCCCC"},
                {"min": 0, "max": 0, "label": '0人', "color": "#ffffff"},
            ],),
        )
    )


map.render_notebook()

以上只是pyecharts的一些基本示例，然而它的强大之处远不止于此。

具体可参考官方文档：https://pyecharts.org/#/zh-cn/intro

python

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python数据可视化之pyecharts

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