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:
- 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
- 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.
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==5.22.0Example:
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.