Python 绘制两个 Plotly hexbin 映射之间的差异

由于在创建图形时以绘图方式确定每个十六进制数，因此您需要访问fig2和fig3中的计数数据.

下面是存储在fig2.data[0]['z']内的数组:

array([15.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  5.,  0.,  0.,
        0.,  0.,  0.,  0.,  0., 10.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

我们可以将fig设置为图2的副本，通过获取图2和图3中的计数数组之间的差值(并将其剪裁为0)来创建新数组，并将fig.data[0]['z']设置为该新array.您还需要相应地更新colorbar的最小值和最大值.

import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots

data = pd.DataFrame({
   'Cat': ['t','y','y','t','t','t','t','y','y','y','t','y'],
   'LAT': [5,6,7,5,6,7,5,6,7,5,6,7],
   'LON': [10,11,12,10,11,12,10,11,12,10,11,12],
   })

data = pd.concat([data]*5)

df_t = data[data['Cat'] == 't']
df_y = data[data['Cat'] == 'y']

# fig = make_subplots(
#     rows = 2, 
#     cols = 1, 
#     subplot_titles = ('t', 'y'),
#     specs = [[{"type": "choroplethmapbox"}], [{"type": "choroplethmapbox"}]],
#     vertical_spacing = 0.05,
#     horizontal_spacing = 0.05
#     )

fig2 = ff.create_hexbin_mapbox(data_frame=df_t,
                           lat="LAT", lon="LON",
                           nx_hexagon=5,
                           opacity=0.5,
                           labels={"color": "Point Count"},
                           mapbox_style='carto-positron',
                          )

fig3 = ff.create_hexbin_mapbox(data_frame=df_y,
                           lat="LAT", lon="LON",
                           nx_hexagon=5,
                           opacity=0.5,
                           labels={"color": "Point Count"},
                           mapbox_style='carto-positron',
                          )

fig = go.Figure(fig2)
fig.data[0]['z'] = (fig2.data[0]['z'] - fig3.data[0]['z']).clip(min=0)
cmax, cmin = max(fig.data[0]['z']), min(fig.data[0]['z'])

fig.update_mapboxes(zoom=6, style='carto-positron')
fig.update_layout(height=600, margin=dict(t=20,b=0,l=0,r=0))
fig.update_coloraxes(cmax=cmax, cmin=cmin)
fig.show()

更新:在fig2.data[0]['z']和fig3.data[0]['z']是不同长度的数组的情况下，您需要填充较短的array.我假设填充值为零，并且我们将以相同的方式计算差异.

使用您编辑的样本数据，我们得到图2.data[0][‘z’]是array([15., 5., 10.])，而图3.data[0][‘z’]是array([10., 15., 5., 5.]).因此，我们用0填充数组([15.，5.，10.])以匹配另一个数组的长度，这意味着我们使用array([15., 5., 10., 0.]).我添加了一些代码来填充较短的数组，然后以与前面相同的方式计算差值和剪裁负值.

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots

# data = pd.DataFrame({
#    'Cat': ['t','y','y','t','t','t','t','y','y','y','t','y'],
#    'LAT': [5,6,7,5,6,7,5,6,7,5,6,7],
#    'LON': [10,11,12,10,11,12,10,11,12,10,11,12],
#    })

data = pd.DataFrame({
       'Cat': ['t','y','y','t','t','t','t','y','y','y','t','y','y'],
       'LAT': [5,6,7,5,6,7,5,6,7,5,6,7,8],
       'LON': [10,11,12,10,11,12,10,11,12,10,11,12,8],
       })

data = pd.concat([data]*5)

df_t = data[data['Cat'] == 't']
df_y = data[data['Cat'] == 'y']

fig2 = ff.create_hexbin_mapbox(data_frame=df_t,
                           lat="LAT", lon="LON",
                           nx_hexagon=5,
                           opacity=0.5,
                           labels={"color": "Point Count"},
                           mapbox_style='carto-positron',
                           min_count=1
                          )

fig3 = ff.create_hexbin_mapbox(data_frame=df_y,
                           lat="LAT", lon="LON",
                           nx_hexagon=5,
                           opacity=0.5,
                           labels={"color": "Point Count"},
                           mapbox_style='carto-positron',
                           min_count=1,
                          )

fig = go.Figure(fig2)
fig2_values, fig3_values = fig2.data[0]['z'], fig3.data[0]['z']

## we pad whichever figure has fewer z values
if len(fig2_values) < len(fig3_values):
   pad_length = len(fig3_values) - len(fig2_values)
   fig2_values = np.pad(fig2_values, (0, pad_length), 'constant')
elif len(fig2_values) > len(fig3_values):
   pad_length = len(fig2_values) - len(fig3_values)
   fig3_values = np.pad(fig3_values, (0, pad_length), 'constant')
else:
   pass

fig.data[0]['z'] = (fig2_values - fig3_values).clip(min=0)
cmax, cmin = max(fig.data[0]['z']), min(fig.data[0]['z'])

fig.update_mapboxes(zoom=6, style='carto-positron')
fig.update_layout(height=600, margin=dict(t=20,b=0,l=0,r=0))
fig.update_coloraxes(cmax=cmax, cmin=cmin)
fig.show()