15 gars
GARS 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]:
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# %pip install vgridpandas
# %pip install vgridpandas
Latlong to GARS¶
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import pandas as pd
from vgridpandas import garspandas
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 = 3
df = df.gars.latlon2gars(resolution)
df.head()
import pandas as pd
from vgridpandas import garspandas
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 = 3
df = df.gars.latlon2gars(resolution)
df.head()
Out[2]:
| lon | lat | passenger_count | gars_res | |
|---|---|---|---|---|
| gars | ||||
| 213LX17 | -73.993896 | 40.750111 | 1 | 3 |
| 213LX31 | -73.976425 | 40.739811 | 1 | 3 |
| 213LX17 | -73.968704 | 40.754246 | 5 | 3 |
| 213LX18 | -73.863060 | 40.769581 | 5 | 3 |
| 213LX17 | -73.945541 | 40.779423 | 1 | 3 |
Maidenhead to geo boundary¶
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df = df.gars.gars2geo()
df.head()
df = df.gars.gars2geo()
df.head()
Out[3]:
| lon | lat | passenger_count | gars_res | geometry | |
|---|---|---|---|---|---|
| gars | |||||
| 213LX17 | -73.993896 | 40.750111 | 1 | 3 | POLYGON ((-73.91667 40.75, -73.91667 40.83333,... |
| 213LX31 | -73.976425 | 40.739811 | 1 | 3 | POLYGON ((-73.91667 40.66667, -73.91667 40.75,... |
| 213LX17 | -73.968704 | 40.754246 | 5 | 3 | POLYGON ((-73.91667 40.75, -73.91667 40.83333,... |
| 213LX18 | -73.863060 | 40.769581 | 5 | 3 | POLYGON ((-73.83333 40.75, -73.83333 40.83333,... |
| 213LX17 | -73.945541 | 40.779423 | 1 | 3 | POLYGON ((-73.91667 40.75, -73.91667 40.83333,... |
GARS point binning¶
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from vgridpandas import garspandas
import geopandas as gpd
# 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")
resolution = 4
stats = "count"
df_bin = df.gars.garsbin(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)
)
from vgridpandas import garspandas
import geopandas as gpd
# 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")
resolution = 4
stats = "count"
df_bin = df.gars.garsbin(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[4]:
<Axes: >
