Python 如何订购我的数据来制作博克的热图？

我有这样一个数据帧（

mD

）：

Pr      Dt           Cd      Cn      Rn
GT      2017-01-12   60      1       'ZP0'
GT      2017-01-12   60      6       'ZP5'
GT      2017-01-12   61      0       'ZP0'
GT      2017-01-12   61      7       'ZP6'
GT      2017-01-12   65      7       'ZP4'
GT      2017-01-10   63      1       'ZP4'
GT      2017-01-10   65      2       'ZP4'
GT      2017-01-10   68      3       'ZP2'
GT      2017-01-09   62      8       'ZP1'
GT      2017-01-12   62      1       'ZP1'
GT      2017-01-11   62      2       'ZP0'
GT      2017-01-11   60      2       'ZP0'
GT      2017-01-10   66      4       'ZP5'
GT      2017-01-10   60      1       'ZP6'
GT      2017-01-09   68      1       'ZP2'
GT      2017-01-09   65      1       'ZP0'
GT      2017-01-09   62      1       'ZP3'
GT      2017-01-09   62      1       'ZP3'
BW      2017-01-11   61      0       'ZP0'
BW      2017-01-10   61      1       'ZP1'
BW      2017-01-10   61      6       'ZP0'
BW      2017-01-11   61      0       'ZP5'
BW      2017-01-11   62      1       'ZP5'
BW      2017-01-12   62      6       'ZP7'
BW      2017-01-11   60      5       'ZP0'
BW      2017-01-09   66      4       'ZP2'

我想在博克制作一张热图，图中，

x=Cd

（从60到70），

y=Dt

（有日期时间格式）和

values=Cn

。换句话说，我想要每个产品（

Pr

）的热图，其中绘制了代码（

Cd

）与日期（

Dt

）的对比，颜色必须表示每个日期和代码的计数总和（

Cn

）

我的第一次尝试是这样的（当然是在导入库之后）：

但是，这将导致一个空白画布。我尝试使用

.groupby

、

pd.pivot\u table

和

pd.crosstab

对数据进行排序，但到目前为止我失败了。我是Bokeh的新手，所以，有人有什么建议吗？

编辑：Bokeh图表回购不再维护，因此如果您想使用Bokeh的最新版本，您应该使用HoloView，或者编写自己的代码来制作热图。

尝试下面的代码，似乎可以工作。
注意：如果您想为每种颜色单独绘制热图，只需过滤df并创建单独的绘图即可。 或者创建一个小部件，让您选择希望可视化的数据集

import pandas as pd
from bokeh.charts import HeatMap, bins
from bokeh.plotting import figure, output_file, show,ColumnDataSource
from bokeh.models import DatetimeTickFormatter

Dt = ['2017-01-12','2017-01-12','2017-01-12','2017-01-12','2017-01-12',
      '2017-01-10','2017-01-10','2017-01-10','2017-01-09','2017-01-12',
      '2017-01-11','2017-01-11','2017-01-10','2017-01-10','2017-01-09',
      '2017-01-09','2017-01-09','2017-01-09','2017-01-11','2017-01-10',
      '2017-01-10','2017-01-11','2017-01-11','2017-01-12','2017-01-11',
      '2017-01-09']
Pr = ['GT','GT','GT','GT','GT','GT','GT','GT','GT','GT','GT','GT','GT','GT',
      'GT','GT','GT','GT','BW','BW','BW','BW','BW','BW', 'BW', 'BW']
Cd = [60,60,61,61,65,63,65,68,62,62,62,60,66,60,68,65,62,62,61,61,61,61,62,62,60,66]
Cn = [1,6,0,7,7,1,2,3,8,1,2,2,4,1,1,1,1,1,0,1,6,0,1,6,5,4]

df = pd.DataFrame({'Pr':Pr,'Dt':Dt,'Cd':Cd,'Cn':Cn})
datadict = df.to_dict(orient='list')
source = ColumnDataSource(datadict)

h1 = HeatMap(source.data, x=bins('Cd'), y='Dt', values='Cn')
show(h1)

import pandas as pd
from bokeh.charts import HeatMap, bins
from bokeh.plotting import figure, output_file, show,ColumnDataSource
from bokeh.models import DatetimeTickFormatter

Dt = ['2017-01-12','2017-01-12','2017-01-12','2017-01-12','2017-01-12',
      '2017-01-10','2017-01-10','2017-01-10','2017-01-09','2017-01-12',
      '2017-01-11','2017-01-11','2017-01-10','2017-01-10','2017-01-09',
      '2017-01-09','2017-01-09','2017-01-09','2017-01-11','2017-01-10',
      '2017-01-10','2017-01-11','2017-01-11','2017-01-12','2017-01-11',
      '2017-01-09']
Pr = ['GT','GT','GT','GT','GT','GT','GT','GT','GT','GT','GT','GT','GT','GT',
      'GT','GT','GT','GT','BW','BW','BW','BW','BW','BW', 'BW', 'BW']
Cd = [60,60,61,61,65,63,65,68,62,62,62,60,66,60,68,65,62,62,61,61,61,61,62,62,60,66]
Cn = [1,6,0,7,7,1,2,3,8,1,2,2,4,1,1,1,1,1,0,1,6,0,1,6,5,4]

df = pd.DataFrame({'Pr':Pr,'Dt':Dt,'Cd':Cd,'Cn':Cn})
datadict = df.to_dict(orient='list')
source = ColumnDataSource(datadict)

h1 = HeatMap(source.data, x=bins('Cd'), y='Dt', values='Cn')
show(h1)