03 a5

A5Pandas 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 A5¶

In [2]:

import pandas as pd
from vgridpandas import a5pandas
df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv')
df = df.head(100)
df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']]
resolution = 16
df = df.a5.latlon2a5(resolution, set_index=False)
df.head()

import pandas as pd from vgridpandas import a5pandas df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv') df = df.head(100) df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']] resolution = 16 df = df.a5.latlon2a5(resolution, set_index=False) df.head()

Out[2]:

	lon	lat	passenger_count	a5	a5_res
0	-73.993896	40.750111	1	2610738938000000	16
1	-73.976425	40.739811	1	2610762d18000000	16
2	-73.968704	40.754246	5	26107685e8000000	16
3	-73.863060	40.769581	5	26107bde88000000	16
4	-73.945541	40.779423	1	2610730008000000	16

A5 to geo boundary¶

In [3]:

df = df.a5.a52geo()
df.head()

df = df.a5.a52geo() df.head()

Out[3]:

	lon	lat	passenger_count	a5	a5_res	geometry
0	-73.993896	40.750111	1	2610738938000000	16	POLYGON ((-73.9936 40.74975, -73.99276 40.7499...
1	-73.976425	40.739811	1	2610762d18000000	16	POLYGON ((-73.97742 40.73934, -73.97652 40.739...
2	-73.968704	40.754246	5	26107685e8000000	16	POLYGON ((-73.96812 40.75402, -73.96727 40.754...
3	-73.863060	40.769581	5	26107bde88000000	16	POLYGON ((-73.86343 40.76952, -73.86252 40.769...
4	-73.945541	40.779423	1	2610730008000000	16	POLYGON ((-73.9443 40.77974, -73.94521 40.7797...

(Multi)Linestring/ (Multi)Polygon to A5¶

In [4]:

import geopandas as gpd
from vgridpandas import a5pandas
gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 16
gdf_polyfill = gdf.a5.polyfill(resolution, compact = False, predicate = "within", explode = True)
gdf_polyfill.head()
gdf_polyfill = gdf_polyfill.a5.a52geo("a5")
gdf_polyfill.plot(edgecolor = "white")

import geopandas as gpd from vgridpandas import a5pandas gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 16 gdf_polyfill = gdf.a5.polyfill(resolution, compact = False, predicate = "within", explode = True) gdf_polyfill.head() gdf_polyfill = gdf_polyfill.a5.a52geo("a5") gdf_polyfill.plot(edgecolor = "white")

Out[4]:

<Axes: >

A5 point binning¶

In [5]:

import pandas as pd
import geopandas as gpd
from vgridpandas import a5pandas
resolution = 15
df = pd.read_csv("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/csv/dist1_pois.csv")
stats = "count"
df_bin = df.a5.a5bin(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 a5pandas resolution = 15 df = pd.read_csv("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/csv/dist1_pois.csv") stats = "count" df_bin = df.a5.a5bin(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: >