如何让仪表盘结论先行

# 导入工具包
import pandas as pd
import numpy as np
import pandas_bokeh
# 导入工具包
import pandas as pd
import numpy as np
import pandas_bokeh
data = pd.read_excel('./data/青岛短租数据整理.xlsx',index_col=0)

data = pd.read_excel('./data/青岛短租数据整理.xlsx',index_col=0)
data.head()

result1 = data.groupby('房间数').agg({'价格':'mean','名称':'count'}).round(0)
result1.columns=['均价','房源数量']
result1.head()

data.价格.describe()

p1 = result1[['均价']].plot_bokeh.bar(title='不同房间数均价',
                                    zooming=False,
                                    panning=False,
                                    toolbar_location=None,
                                    color='#8ac4d0',
                                    legend=None,
                                    show_figure=False,
                                   )

p2 = result1[['房源数量']].plot_bokeh.pie(title='不同房间数房源数量',
                                      zooming=False,
                                      panning=False,
                                      toolbar_location=None,
                                      colormap=['#8ac4d0','#28527a','#f4d160'],
                                      show_figure=False,
                                     )

p3 = data[['价格']].plot_bokeh.hist(title='价格分布',
                                 xlim=(0,1200),  # x轴的刻度
                                 bins=np.linspace(0, 1200, 25),
                                 zooming=False,
                                 panning=False,
                                 legend=None,
                                 toolbar_location=None,
                                 color='#8ac4d0',
                                 show_figure=False,
                                 alpha=1,
                                     )

data["size"] = data["价格"] / 50
p4 = data.plot_bokeh.map(x="经度_re",
                    y="纬度_re",
                    hovertool_string="""<h2> @{名称} </h2> 
                    <h3> 价格: @{价格} </h3>""",
                    tile_provider="CARTODBPOSITRON",
                    size="size", 
                    show_colorbar=True,
                    color='#8ac4d0',
                    legend=None,
                    title="房源分布",
                    toolbar_location=None,
                    show_figure=False,
                        )

组合

p1.plot_width = 300
p2.plot_width = 300
p3.plot_width = 1100
p4.plot_width = 1100

p1.axis.axis_line_color=None
p1.axis.minor_tick_line_color=None
p1.axis.major_tick_line_color=None
p3.axis.axis_line_color=None
p3.axis.minor_tick_line_color=None
p3.axis.major_tick_line_color=None
p4.axis.axis_line_color=None
p4.axis.minor_tick_line_color=None
p4.axis.major_tick_line_color=None
p4.axis.major_label_text_color= None

layout = pandas_bokeh.column(pandas_bokeh.row(p1,p3),
                            pandas_bokeh.row(p2,p4))
pandas_bokeh.output_file('青岛短租分布V1.html')
pandas_bokeh.show(layout)

添加结论

from bs4 import BeautifulSoup
soup = BeautifulSoup(open('青岛短租分布V1.html'),'lxml')

# 结论内容
bt = '青岛短租价格分析'
jl1 = '青岛短租均价300元, 主要集中在150~300元之间,'
jl2 = '1. 房间数量为1的房源最多'
jl3 = '2. 主要集中在沿海风景较好的地段'
jl4 = '数据源: 短租网; 更新日期: 2021-3-12'

html = """
<h3>{0}</h3>
<p style="color:#8ac4d0;line-height: 0em">{1}</p>
<p style="font-size:8px";>{2}</p>
<p style="font-size:8px";>{3}</p>
<p style="font-size:6px;font-family:楷体">{4}</p>
""".format(bt,jl1,jl2,jl3,jl4) + str(soup)


# 字体样式更多设置 font-family:Calibri 或者 '"微软雅黑"'  字体 color:red 颜色  font-weight: bold
# text-indent:2em 缩进 line-height: 2em 行间距;text-align:center 字体居中

# 保存数据
with open("青岛短租分布V2.html" , "w",encoding="utf-8") as f:
    f.write(html)
  
# 预览文件    
import webbrowser
webbrowser.open("青岛短租分布V2.html")

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