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:

Install vgridpandas¶

In [1]:

# %pip install vgridpandas

# %pip install vgridpandas

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

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

In [4]:

import geopandas as gpd
from vgridpandas import h3pandas

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 from vgridpandas import h3pandas 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
import geopandas as gpd
from vgridpandas import h3pandas
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 import geopandas as gpd from vgridpandas import h3pandas 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: >