02 s2

S2Pandas 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 S2¶

In [9]:

import pandas as pd
from vgridpandas import s2pandas
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.s2.latlon2s2(resolution)
df.head()

import pandas as pd from vgridpandas import s2pandas 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.s2.latlon2s2(resolution) df.head()

Out[9]:

	lon	lat	passenger_count	s2_res
s2
89c25	-73.993896	40.750111	1	8
89c25	-73.976425	40.739811	1	8
89c25	-73.968704	40.754246	5	8
89c25	-73.863060	40.769581	5	8
89c25	-73.945541	40.779423	1	8

S2 to geo boundary¶

In [10]:

df = df.s2.s22geo() # if set_index = False in the previous step, call df = df.s2.s22geo(s2_col = 's2_00')
df.head()

df = df.s2.s22geo() # if set_index = False in the previous step, call df = df.s2.s22geo(s2_col = 's2_00') df.head()

Out[10]:

	lon	lat	passenger_count	s2_res	geometry
s2
89c25	-73.993896	40.750111	1	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...
89c25	-73.976425	40.739811	1	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...
89c25	-73.968704	40.754246	5	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...
89c25	-73.863060	40.769581	5	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...
89c25	-73.945541	40.779423	1	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...

(Multi)Linestring/ (Multi)Polygon to S2¶

In [6]:

import geopandas as gpd
from vgridpandas import s2pandas

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 18
gdf_polyfill = gdf.s2.polyfill(resolution, compact = True, predicate = "largest_overlap", explode = True)
gdf_polyfill.head()
gdf_polyfill = gdf_polyfill.s2.s22geo("s2")
gdf_polyfill.plot(edgecolor = "white")

import geopandas as gpd from vgridpandas import s2pandas gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 18 gdf_polyfill = gdf.s2.polyfill(resolution, compact = True, predicate = "largest_overlap", explode = True) gdf_polyfill.head() gdf_polyfill = gdf_polyfill.s2.s22geo("s2") gdf_polyfill.plot(edgecolor = "white")

Out[6]:

<Axes: >

S2 point binning¶

In [8]:

import pandas as pd
import geopandas as gpd
from vgridpandas import s2pandas
resolution = 16
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.s2.s2bin(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 s2pandas resolution = 16 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.s2.s2bin(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[8]:

<Axes: >