比例数据可视化——（pyecharts）

本文数据来自于大数据可视化技术这本书
用于复习

1. 饼图

1.1 基础饼图

import pandas as pd 
from pyecharts.charts import Pie
from pyecharts import options as opts
from pyecharts.globals import ThemeType
data=pd.read_csv("vote_result.csv",encoding="utf-8")
c = (
    Pie(init_opts = opts.InitOpts(theme=ThemeType.WALDEN))#ThemeType.WALDEN用于更换不同风格颜色
    .add("interest",data.values.tolist())
    .set_global_opts(title_opts=opts.TitleOpts(title="用户感兴趣的领域",
                                              subtitle="以下是读者的投票结果。\n读者对金融、医疗保健、市场三个领域感兴趣",
                                              pos_left="left" ),
                    legend_opts=opts.LegendOpts(pos_left="left",
                                                pos_top="middle",
                                                orient="vertical")
)
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
     .render("pie.html")
)

1. 2.玫瑰图

import pandas as pd 
from pyecharts.charts import Pie
from pyecharts import options as opts
from pyecharts.globals import ThemeType
data=pd.read_csv("vote_result.csv",encoding="utf-8")

p=(
    Pie(init_opts=opts.InitOpts(theme=ThemeType.ESSOS))
    .add(" ",data.values.tolist(),
        radius=["30%", "75%"],
        center=["55%", "50%"],
        rosetype="radius",
        label_opts=opts.LabelOpts(is_show=False),
        )
    .set_global_opts(title_opts=opts.TitleOpts(title="用户感兴趣的领域",
                                              subtitle="以下是读者的投票结果。\n读者对金融、医疗保健、市场三个领域感兴趣",
                                              pos_left="left" ),
                    legend_opts=opts.LegendOpts(pos_left="left",
                                                pos_top="middle",
                                                orient="vertical")
)
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
     .render("pie_rose.html")
)

2.柱状堆叠图

2.1 基本堆叠数据图
根据对奥巴马支持率的民意调查数据president_approval_rate.csv进行堆叠柱形图的绘制

from pyecharts.charts import Bar
import pandas as pd
from pyecharts import options as opts
from pyecharts.globals import ThemeType
pre_approval_rate = pd.read_csv("presidential_approval_rate.csv")
bar = (
    Bar(init_opts = opts.InitOpts(theme = ThemeType.DARK))
    .add_xaxis(pre_approval_rate['political_issue'].tolist())
    .add_yaxis("支持", pre_approval_rate['support'].tolist(), stack="1", category_gap="50%")
    .add_yaxis("反对", pre_approval_rate['oppose'].tolist(), stack="1", category_gap="50%")
    .add_yaxis("不发表意见", pre_approval_rate['no_opinion'].tolist(), stack="1", category_gap="50%")
    .set_series_opts(label_opts=opts.LabelOpts(is_show=True))
    .set_global_opts(title_opts=opts.TitleOpts(title = "柱状堆叠图"),
                    xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=30)))
)
bar.render_notebook()

2.2

from pyecharts.charts import Bar
from pyecharts.globals import ThemeType
import pandas as pd
pre_approval_rate = pd.read_csv("presidential_approval_rate.csv")

list_support=['支持','反对','不发表意见']
bar = Bar(init_opts = opts.InitOpts(theme = ThemeType.DARK))
bar.add_xaxis(list_support)

for i in range(pre_approval_rate.iloc[:,0].size):
    issue = pre_approval_rate.loc[i,'political_issue']
    bar.add_yaxis(issue, [int(x) for x in pre_approval_rate.loc[i,['support','oppose','no_opinion']]], 
                  stack=True, category_gap="50%")
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
bar.set_global_opts(title_opts=opts.TitleOpts(title = "柱状图数据堆叠示例", pos_left='center'),
                    legend_opts=opts.LegendOpts(orient="vertical", pos_right="5%"))
bar.render_notebook()

2.3 非堆叠图形展示

3.时堆叠比例图

对1860-2005年人口老龄化数据us_population_by_age.csv进行图形绘制

import pyecharts.options as opts
from pyecharts.charts import Line
import pandas as pd
from pyecharts.globals import ThemeType 

year_population_age = pd.read_csv('us_population_by_age.csv')
#面积折线图
( 
    Line(init_opts=opts.InitOpts(theme=ThemeType.ROMANTIC))
    .add_xaxis(xaxis_data=year_population_age['year'].tolist())
    .add_yaxis(
        series_name="5岁以下",
#         stack="总量",
        y_axis=year_population_age['year_under5'].tolist(),
        label_opts=opts.LabelOpts(is_show=False),
    )
    .add_yaxis(
        series_name="5岁至19岁",
#         stack="总量",
        y_axis=year_population_age['year5_19'].tolist(),
        label_opts=opts.LabelOpts(is_show=False),
    )
    .add_yaxis(
        series_name="20至44岁",
#         stack="总量",
        y_axis=year_population_age['year20_44'].tolist(),
        label_opts=opts.LabelOpts(is_show=False),
    )
    .add_yaxis(
        series_name="45至64岁",
#         stack="总量",
        y_axis=year_population_age['year45_64'].tolist(),
        label_opts=opts.LabelOpts(is_show=False)
    )
    .add_yaxis(
        series_name="65岁以上",
#         stack="总量",
        y_axis=year_population_age['year65above'].tolist(),
        label_opts=opts.LabelOpts(is_show=True, position="top"),
    )
    .set_series_opts(
        areastyle_opts=opts.AreaStyleOpts(opacity=0.5),#填充面积
        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="人口老龄化面积折线图"),
        legend_opts=opts.LegendOpts(type_="scroll",pos_top="8%", pos_left="10%", orient="horizontal"),#图例配置项
        tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
        yaxis_opts=opts.AxisOpts(
            type_="value",
            axistick_opts=opts.AxisTickOpts(is_show=True),
            splitline_opts=opts.SplitLineOpts(is_show=True),
        ),
        xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=True,axislabel_opts=opts.LabelOpts(rotate=30))
    )
    .render_notebook()
)