CODEWITHSUNDEEP
pythonpandasgeopandas
sjp_1989
sjp_1989

Reputation: 137

How can I do a sjoin iteratively over features in a shapefile with geopandas, then encode categorical data?

I have two shapefiles (https://drive.google.com/drive/folders/1pbvKvhIIvhqHfcMe9g6qtsjbZ6SzZrqt?usp=sharing) - one point layer, and one polygon layer. The point layer represents customers and their location, while the polygon layers represents two boundaries. The objective is to get a table in the following format:

customer location 1 location 2
1 1 1
2 0 1
3 1 1
5 1 0
6 1 0
9 0 0
10 0 0

The way I've thought of doing this is to iterate through the polygons and do a sjoin with the points, then encode the categories as such:

import geopandas as gpd

points = gpd.read_file('point.shp')
polygons = gpd.read_file('polygon.shp')

for index,row in polygons.iterrows():
    points = gpd.sjoin(points, row, how='left', op='intersects')
    points = pd.get_dummies(points, columns=['name'])

I get this error message:

ValueError: 'right_df' should be GeoDataFrame, got <class 'pandas.core.series.Series'>

Appreciate any advice, thanks in advance!

Upvotes: 1

Views: 844

Answers (1)

ojdo
ojdo

Reputation: 8900

You do not need a join, the intersects method is enough. Your target structure can be achieved using:

points_in_locations = points.copy()
for idx, row in polygons.iterrows():
    is_in_polygon = points.intersects(row.geometry)
    points_in_locations[f"location {idx + 1}"] = is_in_polygon.astype(int)

resulting in:

   id                   geometry  location 1  location 2
0   1  POINT (103.87728 1.30449)           0           1
1   2  POINT (103.87723 1.30415)           0           1
2   3  POINT (103.87761 1.30408)           0           1
3   1  POINT (103.87680 1.30287)           1           0
4   5  POINT (103.87724 1.30288)           1           0
5   6  POINT (103.87710 1.30275)           1           0
6   3  POINT (103.87687 1.30270)           1           0
7   9  POINT (103.87669 1.30444)           0           0
8  10  POINT (103.87681 1.30396)           0           0

Upvotes: 1

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