figure_factory create_hexbin_mapbox ignored agg_func

As the title states, the aggregation function is entirely ignored and it does not make any difference whether you insert np.mean, np.min or np.max. Using plotly==5.22.0

Example:

1. Generate data

import plotly.figure_factory as ff
import pandas as pd
import numpy as np

# Mock GeoDataFrame with latitude, longitude, and value columns
np.random.seed(0)
num_points = 1000
data = {
    'lat': np.random.uniform(40, 45, num_points),
    'lon': np.random.uniform(-75, -70, num_points),
    'value': np.random.uniform(0, 1, num_points)
}
df = pd.DataFrame(data)
df

2. Plot and check vals

fig = ff.create_hexbin_mapbox(
    data_frame=df, lat="lat", lon="lon",
    nx_hexagon=50,  # Decrease the size of hexagons
    opacity=0.5, labels={"color": "value"},
    color_continuous_scale="Viridis",
    agg_func=np.min,  # or np.max aggregation
    show_original_data=True,
    original_data_marker=dict(size=1.1, opacity=0.6, color="deeppink")
)

# Extract the hexbin data
hexbin_data = fig.data[0]

# Check the hexbin values
print("Hexbin values (z):", hexbin_data.z)

# Update the text of each hexagon to display the maximum value
hexbin_data.hovertemplate = 'Value: %{z}<extra></extra>'

# Update the layout to use OSM tiles
fig.update_layout(
    mapbox_style="open-street-map",
    height=800  # Set the desired height
)

fig.show()

Output for np.min, np.mean and np.max is identical:

Hexbin values (z): [0. 0. 0. ... 0. 1. 0.]

Hence, the plot does not change.

Start at the figure_factory entry point ff.create_hexbin_mapbox and reproduce the issue with the provided DataFrame and np.min, np.mean, and np.max examples. Trace how agg_func is passed into hexbin generation, then verify that the resulting hexbin_data.z values differ appropriately for each aggregation function.