01 h3

H3Pandas key features¶

You can try out vgridpandas by using the cloud-computing platforms below without having to install anything on your computer:

Full VgridPandas DGGS documentation is available at vgridpandas document.

To work with Vgrid in Python or CLI, use vgrid package. Full Vgrid DGGS documentation is available at vgrid document.

To work with Vgrid DGGS in QGIS, install the Vgrid Plugin.

To visualize DGGS in Maplibre GL JS, try the vgrid-maplibre library.

For an interactive demo, visit the Vgrid Homepage.

Install vgridpandas¶

Uncomment the following line to install vgridpandas.

In [1]:

# %pip install vgridpandas

# %pip install vgridpandas

Latlon to H3¶

In [2]:

import pandas as pd
from vgridpandas import h3pandas
df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv')
df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']]
df = df.head(100)
resolution = 8
df = df.h3.latlon2h3(resolution)
df.head()

import pandas as pd from vgridpandas import h3pandas df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv') df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']] df = df.head(100) resolution = 8 df = df.h3.latlon2h3(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	h3	h3_res
0	-73.993896	40.750111	1	882a100d2dfffff	8
1	-73.976425	40.739811	1	882a100d2bfffff	8
2	-73.968704	40.754246	5	882a100d63fffff	8
3	-73.863060	40.769581	5	882a100e25fffff	8
4	-73.945541	40.779423	1	882a10089bfffff	8

H3 to geo boundary¶

In [3]:

df = df.h3.h32geo()
df.head()

df = df.h3.h32geo() df.head()

Out[3]:

	lon	lat	passenger_count	h3	h3_res	geometry
0	-73.993896	40.750111	1	882a100d2dfffff	8	POLYGON ((-73.98804 40.75427, -73.99442 40.753...
1	-73.976425	40.739811	1	882a100d2bfffff	8	POLYGON ((-73.9748 40.74405, -73.98118 40.743,...
2	-73.968704	40.754246	5	882a100d63fffff	8	POLYGON ((-73.9689 40.75743, -73.97528 40.7563...
3	-73.863060	40.769581	5	882a100e25fffff	8	POLYGON ((-73.86237 40.77082, -73.86875 40.769...
4	-73.945541	40.779423	1	882a10089bfffff	8	POLYGON ((-73.94629 40.78183, -73.95267 40.780...

(Multi)Linestring/ (Multi)Polygon to H3¶

In [4]:

import geopandas as gpd

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 10
gdf_polyfill = gdf.h3.polyfill(resolution, compact = True, predicate = "largest_overlap", explode = True)
gdf_polyfill.head()
gdf_polyfill = gdf_polyfill.h3.h32geo("h3")
gdf_polyfill.plot(edgecolor = "white")

import geopandas as gpd gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 10 gdf_polyfill = gdf.h3.polyfill(resolution, compact = True, predicate = "largest_overlap", explode = True) gdf_polyfill.head() gdf_polyfill = gdf_polyfill.h3.h32geo("h3") gdf_polyfill.plot(edgecolor = "white")

Out[4]:

<Axes: >

H3 point binning¶

In [5]:

import pandas as pd
resolution = 10
df = pd.read_csv("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/csv/dist1_pois.csv")
# df = gpd.read_file("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/dist1_pois.geojson")
df.head()
stats = "count"
df_bin = df.h3.h3bin(resolution=resolution, stats = stats, 
                    # numeric_column="confidence",
                    # category_column="category",
                    return_geometry=True)
df_bin.plot(
    column=stats,        # numeric column to base the colors on
    cmap='Spectral_r',        # color scheme (matplotlib colormap)
    legend=True,  
    linewidth=0.2          # boundary width (optional)
)

import pandas as pd resolution = 10 df = pd.read_csv("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/csv/dist1_pois.csv") # df = gpd.read_file("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/dist1_pois.geojson") df.head() stats = "count" df_bin = df.h3.h3bin(resolution=resolution, stats = stats, # numeric_column="confidence", # category_column="category", return_geometry=True) df_bin.plot( column=stats, # numeric column to base the colors on cmap='Spectral_r', # color scheme (matplotlib colormap) legend=True, linewidth=0.2 # boundary width (optional) )

Out[5]:

<Axes: >