GeoPandas examples

In the following examples we use the nybb dataset.

GeoPandas-Plot

  1. Import

[1]:

import geopandas as gpd

from geodatasets import get_path

  1. Read data

[2]:

nybb = get_path("nybb")

[3]:

df = gpd.read_file(nybb)

  1. Display the first lines

[4]:

df.head()

[4]:
BoroCode BoroName Shape_Leng Shape_Area geometry
0 5 Staten Island 330470.010332 1.623820e+09 MULTIPOLYGON (((970217.022 145643.332, 970227....
1 4 Queens 896344.047763 3.045213e+09 MULTIPOLYGON (((1029606.077 156073.814, 102957...
2 3 Brooklyn 741080.523166 1.937479e+09 MULTIPOLYGON (((1021176.479 151374.797, 102100...
3 1 Manhattan 359299.096471 6.364715e+08 MULTIPOLYGON (((981219.056 188655.316, 980940....
4 2 Bronx 464392.991824 1.186925e+09 MULTIPOLYGON (((1012821.806 229228.265, 101278...

  1. Plotting

[5]:

ax = df.plot(figsize=(10, 10), alpha=0.5, edgecolor="k")
../../_images/matplotlib_geopandas_example_11_0.png

figsize specifies the size of the plot, alpha the transparency and edgecolor the edge colour.

Plotting in Folium

Folium, or more precisely the leaflet.js uses the values for latitude and longitude by default. We therefore have to convert our values first:

  1. Determine coordinates

    To be able to use the data with EPSG codes, GeoPandas offers geopandas.GeoDataFrame.crs (spatial reference system).

[6]:

print(df.crs)

EPSG:2263

[7]:

df = df.to_crs(epsg=4326)
print(df.crs)
df.head()

EPSG:4326

[7]:
BoroCode BoroName Shape_Leng Shape_Area geometry
0 5 Staten Island 330470.010332 1.623820e+09 MULTIPOLYGON (((-74.05051 40.56642, -74.05047 ...
1 4 Queens 896344.047763 3.045213e+09 MULTIPOLYGON (((-73.83668 40.59495, -73.83678 ...
2 3 Brooklyn 741080.523166 1.937479e+09 MULTIPOLYGON (((-73.86706 40.58209, -73.86769 ...
3 1 Manhattan 359299.096471 6.364715e+08 MULTIPOLYGON (((-74.01093 40.68449, -74.01193 ...
4 2 Bronx 464392.991824 1.186925e+09 MULTIPOLYGON (((-73.89681 40.79581, -73.89694 ... 
[8]:

import folium

[9]:

m = folium.Map(
    location=[40.70, -73.94], zoom_start=10, tiles="CartoDB positron"
)
for _, r in df.iterrows():
    # without simplifying the representation of each borough, the map might not be displayed
    sim_geo = gpd.GeoSeries(r["geometry"]).simplify(tolerance=0.001)
    geo_j = sim_geo.to_json()
    geo_j = folium.GeoJson(
        data=geo_j,
    )
    folium.Popup(r["BoroName"]).add_to(geo_j)
    geo_j.add_to(m)
m

[9]:
Make this Notebook Trusted to load map: File -> Trust Notebook