In this article, we will show you how to filter a GeoPandas dataframe based on latitude or longitude.
Using GeoPandas query()
First, we will create a GeoPandas dataframe. We will use the cities dataset from the GeoPandas library which contains information about cities from around the world.
import geopandasimport pandas as pddf = pd.DataFrame({"City": ["Paris", "Brasilia", "Beijing", "Moscow", "Caracas"],"Latitude": [-48.51, -15.78, -39.55, -55.45, 90],"Longitude": [-2.20, -47.91, -116.25, -37.36, 180],})gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude), crs="EPSG:4326")print(gdf.head())
Next, we can select rows in our dataframe based on their latitude or longitude values using a query. A query allows us to specify conditions for our search. For example, we can select all cities with a latitude greater than 0:
gdf.query('Latitude > 0')
returns
City Latitude Longitude geometry4 Caracas 90.0 180.0 POINT (180.00000 90.00000)
Note that
gdf[ gdf['Latitude'] > 0 ]
also work and returns the same output.
We can also use queries to select cities within a certain range of longitude and latitude values:
gdf.query('-40 < Latitude < 0')
returns
City Latitude Longitude geometry4 Caracas 90.0 180.0 POINT (180.00000 90.00000)
Now, our dataframe only contains cities within the specified range.
An example of how to use geopandas dataframe filtering
import matplotlib.pyplot as pltworld = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))world.crs
Output
Geographic 2D CRS: EPSG:4326>Name: WGS 84Axis Info [ellipsoidal]:- Lat[north]: Geodetic latitude (degree)- Lon[east]: Geodetic longitude (degree)Area of Use:- name: World.- bounds: (-180.0, -90.0, 180.0, 90.0)Datum: World Geodetic System 1984 ensemble- Ellipsoid: WGS 84- Prime Meridian: Greenwichprint(world)
Ouput
pop_est continent name iso_a3 gdp_md_est \0 889953.0 Oceania Fiji FJI 54961 58005463.0 Africa Tanzania TZA 631772 603253.0 Africa W. Sahara ESH 9073 37589262.0 North America Canada CAN 17364254 328239523.0 North America United States of America USA 21433226.. ... ... ... ... ...172 6944975.0 Europe Serbia SRB 51475173 622137.0 Europe Montenegro MNE 5542174 1794248.0 Europe Kosovo -99 7926175 1394973.0 North America Trinidad and Tobago TTO 24269176 11062113.0 Africa S. Sudan SSD 11998geometry0 MULTIPOLYGON (((180.00000 -16.06713, 180.00000...1 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...2 POLYGON ((-8.66559 27.65643, -8.66512 27.58948...3 MULTIPOLYGON (((-122.84000 49.00000, -122.9742...4 MULTIPOLYGON (((-122.84000 49.00000, -120.0000..... ...172 POLYGON ((18.82982 45.90887, 18.82984 45.90888...173 POLYGON ((20.07070 42.58863, 19.80161 42.50009...174 POLYGON ((20.59025 41.85541, 20.52295 42.21787...175 POLYGON ((-61.68000 10.76000, -61.10500 10.890...176 POLYGON ((30.83385 3.50917, 29.95350 4.17370, ...[177 rows x 6 columns]
Plot the data
ax = world.plot()ax.set_title("WGS84 (lat/lon)");
world['longitude'] = world['geometry'].centroid.xworld['latitude'] = world['geometry'].centroid.yprint( world )
Output
pop_est continent name iso_a3 gdp_md_est \0 889953.0 Oceania Fiji FJI 54961 58005463.0 Africa Tanzania TZA 631772 603253.0 Africa W. Sahara ESH 9073 37589262.0 North America Canada CAN 17364254 328239523.0 North America United States of America USA 21433226.. ... ... ... ... ...172 6944975.0 Europe Serbia SRB 51475173 622137.0 Europe Montenegro MNE 5542174 1794248.0 Europe Kosovo -99 7926175 1394973.0 North America Trinidad and Tobago TTO 24269176 11062113.0 Africa S. Sudan SSD 11998geometry longitude \0 MULTIPOLYGON (((20037508.343 -1812498.413, 200... 1.824878e+071 POLYGON ((3774143.866 -105758.362, 3792946.708... 3.869296e+062 POLYGON ((-964649.018 3205725.605, -964597.245... -1.348403e+063 MULTIPOLYGON (((-13674486.249 6274861.394, -13... -1.079779e+074 MULTIPOLYGON (((-13674486.249 6274861.394, -13... -1.329713e+07.. ... ...172 POLYGON ((2096126.508 5765757.958, 2096127.988... 2.316658e+06173 POLYGON ((2234260.104 5249565.284, 2204305.520... 2.146954e+06174 POLYGON ((2292095.761 5139344.949, 2284604.344... 2.326059e+06175 POLYGON ((-6866186.192 1204901.071, -6802177.4... -6.827259e+06176 POLYGON ((3432408.751 390883.649, 3334408.389 ... 3.361521e+06latitude0 -1.958098e+061 -7.003071e+052 2.794163e+063 1.044422e+074 6.667416e+06.. ...172 5.505001e+06173 5.280332e+06174 5.248463e+06175 1.167359e+06176 8.153257e+05[176 rows x 8 columns]
We will now convert geometries to a different coordinate reference system called 3857 (mercator projection).
world = world.to_crs(3857)ax = world.plot()
In order to improve the Mercator projection map, we should exclude the rows that have a latitude value exceeding 80:
world = world[ world['latitude'] > -80.0 ]print(world)
then
ax = world.plot()plt.savefig("mercator.png", bbox_inches='tight', dpi=200)ax.set_title("Mercator");
will generate the following figure
References
| Links | Site |
|---|---|
| query() | pandas.pydata.org |
| centroid() | geopandas.org |
| to_crs() | geopandas.org |
