Hexagonal Heatmap

import pandas as pd

from lets_plot import *

LetsPlot.setup_html()

df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/refs/heads/master/data/diamonds.csv")
print(df.shape)
df.head()

(53940, 10)

	carat	cut	color	clarity	depth	table	price	x	y	z
0	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

Default View

p = ggplot(df, aes("carat", "price")) + scale_fill_viridis(trans='symlog')

p + geom_hex()

Parameters

p + geom_hex(bins=[5, 10]) + ggtitle("bins=[5, 10]")

p + geom_hex(binwidth=[1, 5000]) + ggtitle("binwidth=[1, 5000]")

p + geom_hex(binwidth=[1, 5000], drop=False) + ggtitle("drop=False")

'binhex' Stat

ggplot(df, aes("carat", "price")) + \
    geom_point(aes(color='..count..'), stat='binhex',
               binwidth=[1, 5000], drop=False,
               size=.75, size_unit='x') + \
    coord_cartesian(xlim=[-1, 7], ylim=[-2_500, 25_000])

Hexagons on Map

df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/italy_cdi.csv")\
    [["CDI", "No. of responses", "Latitude", "Longitude"]]
df.columns = ["CDI", "number_of_responses", "lat", "lon"]
print(df.shape)
df.head()

(27, 4)

	CDI	number_of_responses	lat	lon
0	3.1	1	42.37	14.07
1	2.2	1	42.25	14.37
2	2.0	1	42.20	14.21
3	3.1	1	42.22	14.28
4	3.4	4	42.46	14.21

ggplot(df) + \
    geom_livemap(location=[15, 41], zoom=7) + \
    geom_hex(aes(x="lon", y="lat", weight="number_of_responses", fill="..count.."),
             binwidth=[.5, .5], size=.5, color="#081d58", alpha=.5) + \
    scale_fill_brewer(name="Number of responses", type='seq', palette="YlGnBu") + \
    ggtitle("Community Internet Intensity Map",
            "M 4.7 - 1 km NW of Montagano, Italy") + \
    theme(plot_title=element_text(face='bold'))