Introduction to Geographic Selection in Pandas DataFrames

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As a data scientist or analyst working with geographic data, selecting objects within a specific region from a pandas DataFrame can be a challenging task. In this article, we will explore how to perform this selection using the geopandas library and the spatial join operator.

Background on Geospatial DataFrames

Geospatial data frames are designed to store and manipulate geospatial data, such as geographic points, lines, and polygons. The geopandas library provides a convenient interface for working with these data structures and allows us to perform various operations, including spatial joins.

Creating GeodataFrames

To select objects within a specific region from a pandas DataFrame, we need to create two geodataframes: one containing the polygon that defines our region of interest (ROI) and another containing all points or objects in our original DataFrame. The spatial join operator is used to enable the points that fall inside the ROI to be selected.

Creating the Polygon Geodataframe

To create a polygon geodataframe, we need to define its geometry using the Shapely library. We can use the wkt format to represent our polygon.

# Import necessary libraries
import pandas as pd
import geopandas as gpd
from shapely import wkt
from shapely.geometry import Point, Polygon

# Define the polygon geometry
d = {'poly_id':[1], 'wkt':['POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))']}
df = pd.DataFrame( data=d )
geometry = [loads(pgon) for pgon in df.wkt]
polygon_df = gpd.GeoDataFrame(df, \
                   crs={'init': 'epsg:4326'}, \
                   geometry=geometry)

# Plot the polygon
polygon_df.plot(color='lightgray', zorder=1)

Creating the Points Geodataframe

Next, we need to create a points geodataframe from our original DataFrame.

# Read the CSV file into a pandas DataFrame
locs = pd.read_csv('locations.csv', sep=',')

# Create a points geodataframe
geo_locs = gpd.GeoDataFrame(locs, crs={'init': 'epsg:4326'})
locs_geom = [Point(xy) for xy in zip(geo_locs.LON, geo_locs.LAT)]
geo_locs['wkt'] = geo_locs.apply( lambda x: Point(x.LON, x.LAT), axis=1 )
geo_locs = gpd.GeoDataFrame(geo_locs, crs={'init': 'epsg:4326'}, \
    geometry=geo_locs['wkt'])

# Plot the points
geo_locs.plot(ax=None, color="red")

Performing a Spatial Join

Now that we have created our two geodataframes, we can perform a spatial join using the spatial_join operator.

# Perform a spatial join of geo_locs within polygon_df, get the result in pts_in_poly GeodataFrame.
pts_in_poly = gpd.sjoin(geo_locs, polygon_df, op='within', how='inner')

# Print the ID of the points that fall within the polygon.
print(pts_in_poly.ID)

# The output will be:
#2    3
#3    4
#4    5
#Name: ID, dtype: int64

In this example, we have successfully selected the points that fall within our ROI (defined by the polygon geodataframe).

Plotting the Results

Finally, let’s plot both the polygon and the points to visualize our results.

# Plot the polygon and all the points.
ax1 = polygon_df.plot(color='lightgray', zorder=1)
geo_locs.plot(ax=ax1, zorder=5, color="red")

The resulting plot will show our ROI (the polygon) with all the points that fall within it marked in red.

Conclusion

In this article, we have explored how to select objects within a specific region from a pandas DataFrame using the geopandas library and the spatial join operator. We created two geodataframes: one containing the polygon that defines our ROI and another containing all points or objects in our original DataFrame. By performing a spatial join of these two dataframes, we can easily identify the points that fall within our ROI.

We hope this article has provided you with a practical guide to working with geospatial data in pandas DataFrames. With geopandas, you can perform various geospatial operations and visualize your results using standard cartographic tools.

Last modified on 2025-05-01