Question: I've seen posts relating to plotting the difference between two hexbin maps in matplotlib. I couldn't find anything executing the same process but for Plotly hexbin map box plots. If I have two separate hexbin subplots (t, y), is it possible to produce a single plot that subtracts the difference between t and y? 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.add_trace(fig2.data[0], row=1,col=1) fig.update_mapboxes(zoom=4, style='carto-positron') fig.add_trace(fig3.data[0], row=2,col=1) fig.update_mapboxes(zoom=4, style='carto-positron') fig.update_layout(height=600, margin=dict(t=20,b=0,l=0,r=0)) fig.show()  intended output: The bottom left bin for t has 15 points, while y has 5. So this will total 10. The middle bin has 10 points for both so will result in 0. The top right has 5 for t and 15 for y, coming to -10. But I'll set vmin to 0 to ensure no negative values.  Edit 2: If I alter the input data with different size arrays and include min_count = 1 as a parameter, I return an error. 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], }) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) /var/folders/bf/09nyl3td65j2lty5m7138ndw0000gn/T/ipykernel_78237/2142200526.py in <module> 47 48 fig = go.Figure(fig2) ---> 49 fig.data[0]['z'] = (fig2.data[0]['z'] - fig3.data[0]['z']).clip(min=0) 50 cmax, cmin = max(fig.data[0]['z']), min(fig.data[0]['z']) 51 ValueError: operands could not be broadcast together with shapes (3,) (4,) 

Answer: Since plotly determines the counts within each hexbin when creating the figure, you'll need to access the count data inside both fig2 and fig3. Here is the array as it's stored inside fig2.data[0]['z']: 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.])  We can set fig to be a copy of fig2, create a new array by taking the difference between the count arrays from fig2 and fig3 (and clipping it at 0), and set fig.data[0]['z'] to this new array. You will also want to update the minimum and maximum values of the colorbar accordingly. 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()   Update: in a situation where fig2.data[0]['z'] and fig3.data[0]['z'] are arrays of different lengths, you'll need to pad the shorter array. I am making the assumption that the padding values are zero, and that we will calculate the differences in the same manner. Using your edited sample data, we get that fig2.data[0]['z'] is array([15., 5., 10.]) and fig3.data[0]['z'] is array([10., 15., 5., 5.]). So we pad array([15., 5., 10.]) with 0s to match the length of the other array meaning we use array([15., 5., 10., 0.]). I've added some code which pads the shorter array, then computes the difference and clips negative values the same way as before. 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()