Pyecharts-气泡图

Pyecharts
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气泡图

气泡图

导入所需要的包

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import numpy as np
import pandas as pd
from pyecharts.commons.utils import JsCode
from pyecharts.charts import *
from pyecharts import options as opts

读取数据

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df1 = pd.read_excel("气泡图数据.xlsx", sheet_name=0, header=0)
df2 = pd.read_excel("气泡图数据.xlsx", sheet_name=1, header=0)

数据处理

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data1 = []
for y, s, c in zip(df1['人均寿命'], df1['GDP总量'], df1['国家']):
    data1.append([y, s, c])

data2 = []
for y, s, c in zip(df2['人均寿命'], df2['GDP总量'], df2['国家']):
    data2.append([y, s, c])

另一种处理数据的方法：

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data1 = []
for n, x, y, s in zip(df1['国家'], df1['人均GDP'], df1['人均寿命'], df1['GDP总量']):
    data1.append(opts.ScatterItem(
        name=n,
        value=[x, y],
        symbol_size=math.sqrt(s)/500
    ))

data2 = []
for n, x, y, s in zip(df2['国家'], df2['人均GDP'], df2['人均寿命'], df2['GDP总量']):
    data2.append(opts.ScatterItem(
        name=n,
        value=[x, y],
        symbol_size=math.sqrt(s)/500
    ))

这样同时映射了图形大小，感觉这样更好点。

先画1990年

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scatter1 = (Scatter()
           .add_xaxis(df1['人均GDP'])
           .add_yaxis('1990',
                      data1)
           .set_global_opts(xaxis_opts=opts.AxisOpts(type_='value'),
                            yaxis_opts=opts.AxisOpts(is_scale=True))
            )
scatter1.render("气泡图.html")

JsCode()

可以用以下代码来看参数怎么传递

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item_color_js_2 = """function(x) {console.log(x); return x;}"""

映射GDP总量为不同大小

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size = '''function(x) {return Math.sqrt(x[2])/500}'''

.add_yaxis('1990',
           data1,
           symbol_size=JsCode(size))

设置tooltips

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tool = '''function(x) {return x.seriesName + '-' + x.data[3] + '<br/>'
        +'人均GDP： '+x.data[0]+' 美元<br/>'
        +'GDP总量： '+x.data[2]+' 美元<br/>'
        +'人均寿命： '+x.data[1]+'岁';}'''

.set_global_opts(tooltip_opts=opts.TooltipOpts(formatter=JsCode(tool)))

再画2015年

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scatter2 = (Scatter()
            .add_xaxis(df2['人均GDP'])
            .add_yaxis('2015',
                       data2,
                       symbol_size=JsCode(size))
            )

scatter1.overlap(scatter2)
scatter1.render('气泡图.html')
os.system('气泡图.html')

设置颜色

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item_color_js_1 = """new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{
                                        offset: 0,
                                        color: 'rgb(251, 118, 123)'
                                    }, {
                                        offset: 1,
                                        color: 'rgb(204, 46, 72)'
                                    }])"""

item_color_js_2 = """new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{
                                        offset: 0,
                                        color: 'rgb(129, 227, 238)'
                                    }, {
                                        offset: 1,
                                        color: 'rgb(25, 183, 207)'
                                    }])"""

echarts.graphic.RadialGradient：径向渐变色设置，前三个参数分别是圆心 x, y 和半径，第四个参数是数组, 用于配置颜色的渐变过程. 每一项为一个对象, 包含offset和color两个参数. offset的范围是0 ~ 1, 用于表示位置（offset: 0表示0% 处的颜色；offset: 1表示100% 处的颜色）

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# scatter1设置
.add_yaxis(itemstyle_opts=opts.ItemStyleOpts(color=JsCode(item_color_js_1)))
# scatter2设置
.add_yaxis(itemstyle_opts=opts.ItemStyleOpts(color=JsCode(item_color_js_2)))

去掉标签

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.add_yaxis(label_opts=opts.LabelOpts(is_show=False))

设置坐标轴

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.set_global_opts(xaxis_opts=opts.AxisOpts(type_='value', name='人均GDP'),
                 yaxis_opts=opts.AxisOpts(is_scale=True, name='人均寿命'),

坐标轴类型： ‘value’-连续数据；'category '-离散数据；‘time’-时间轴；‘log’-对数轴

设置阴影

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item_style_1 = {
    'shadowBlur': 10,
    'shadowColor': 'rgba(120, 36, 50, 0.5)',
    'shadowOffsetY': 5,
    'color': JsCode(item_color_js_1)
}

item_style_2 = {
    'shadowBlur': 10,
    'shadowColor': 'rgba(120, 36, 50, 0.5)',
    'shadowOffsetY': 5,
    'color': JsCode(item_color_js_2)
}

scatter1 = (Scatter()
           .add_xaxis(df1['人均GDP'])
           .add_yaxis('1990',
                      data1,
                      symbol_size=JsCode(size),
                      itemstyle_opts=item_style_1)
           .set_global_opts(xaxis_opts=opts.AxisOpts(type_='value'),
                            yaxis_opts=opts.AxisOpts(is_scale=True),
                            tooltip_opts=opts.TooltipOpts(formatter=JsCode(tool)))
           )
scatter2 = (Scatter()
            .add_xaxis(df2['人均GDP'])
            .add_yaxis('2015',
                       data2,
                       symbol_size=JsCode(size),
                       itemstyle_opts=item_style_2)
            )

设置背景颜色、大小

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bg_color_js = """new echarts.graphic.RadialGradient(0.3, 0.3, 0.8, [{
        offset: 0,
        color: '#f7f8fa'
    }, {
        offset: 1,
        color: '#cdd0d5'
    }])"""

Scatter(init_opts=opts.InitOpts(bg_color=JsCode(bg_color_js), width='1000px', height='800px'))

设置分割线

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.set_global_opts(xaxis_opts=opts.AxisOpts(type_='value', name='人均GDP', splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed'))),
                 yaxis_opts=opts.AxisOpts(is_scale=True, name='人均寿命', splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed'))),
                 tooltip_opts=opts.TooltipOpts(formatter=JsCode(tool)))

添加标题、图例位置

.set_global_opts中插入

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legend_opts=opts.LegendOpts(is_show=True, pos_right=10),
title_opts=opts.TitleOpts(title="1990 与 2015 年各国家人均寿命与 GDP")

全代码

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import os
import numpy as np
import pandas as pd
from pyecharts.commons.utils import JsCode
from pyecharts.charts import *
from pyecharts import options as opts

df1 = pd.read_excel("气泡图数据.xlsx", sheet_name=0, header=0)
df2 = pd.read_excel("气泡图数据.xlsx", sheet_name=1, header=0)

data1 = []
for y, s, c in zip(df1['人均寿命'], df1['GDP总量'], df1['国家']):
    data1.append([y, s, c])

data2 = []
for y, s, c in zip(df2['人均寿命'], df2['GDP总量'], df2['国家']):
    data2.append([y, s, c])


size = '''function(x) {return Math.sqrt(x[2])/500}'''

tool = '''function(x) {return x.seriesName + '-' + x.data[3] + '<br/>'
        +'人均GDP： '+x.data[0]+' 美元<br/>'
        +'GDP总量： '+x.data[2]+' 美元<br/>'
        +'人均寿命： '+x.data[1]+'岁';}'''

item_color_js_1 = """new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{
                                        offset: 0,
                                        color: 'rgb(251, 118, 123)'
                                    }, {
                                        offset: 1,
                                        color: 'rgb(204, 46, 72)'
                                    }])"""

item_color_js_2 = """new echarts.graphic.RadialGradient(0.4, 0.3, 1, [{
                                        offset: 0,
                                        color: 'rgb(129, 227, 238)'
                                    }, {
                                        offset: 1,
                                        color: 'rgb(25, 183, 207)'
                                    }])"""
item_style_1 = {
    'shadowBlur': 10,
    'shadowColor': 'rgba(120, 36, 50, 0.5)',
    'shadowOffsetY': 5,
    'color': JsCode(item_color_js_1)
}

item_style_2 = {
    'shadowBlur': 10,
    'shadowColor': 'rgba(120, 36, 50, 0.5)',
    'shadowOffsetY': 5,
    'color': JsCode(item_color_js_2)
}

bg_color_js = '''new echarts.graphic.RadialGradient(0.3, 0.3, 0.8, [
    {
      offset: 0,
      color: '#f7f8fa'
    },
    {
      offset: 1,
      color: '#cdd0d5'
    }
  ])'''

scatter1 = (Scatter(init_opts=opts.InitOpts(bg_color=JsCode(bg_color_js), width='750px', height='600px'))
           .add_xaxis(df1['人均GDP'])
           .add_yaxis('1990',
                      data1,
                      symbol_size=JsCode(size),
                      label_opts=opts.LabelOpts(is_show=False),
                      itemstyle_opts=item_style_1)
           .set_global_opts(xaxis_opts=opts.AxisOpts(type_='value', name='人均GDP',
                                                     splitline_opts=opts.SplitLineOpts(is_show=True,
                                                                                       linestyle_opts=opts.LineStyleOpts(type_='dashed'))),
                            yaxis_opts=opts.AxisOpts(is_scale=True, name='人均寿命',
                                                     splitline_opts=opts.SplitLineOpts(is_show=True,
                                                                                       linestyle_opts=opts.LineStyleOpts(type_='dashed'))),
                            tooltip_opts=opts.TooltipOpts(formatter=JsCode(tool)),
                            legend_opts=opts.LegendOpts(is_show=True, pos_right=10),
                            title_opts=opts.TitleOpts(title="1990 与 2015 年各国家人均寿命与 GDP"))
           )
scatter2 = (Scatter()
            .add_xaxis(df2['人均GDP'])
            .add_yaxis('2015',
                       data2,
                       symbol_size=JsCode(size),
                       label_opts=opts.LabelOpts(is_show=False),
                       itemstyle_opts=item_style_2)
            )

scatter1.overlap(scatter2)
scatter1.render('气泡图.html')
os.system('气泡图.html')

大功告成！

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