I have a geopandas dataframe made up of an id and a geometry column which is populated by 2D points. I want to join the points for each unique id to create a polygon, so that my new dataframe will have polygons as its geometry. My code currently looks something like this:
polygons = geopandas.GeoDataFrame()
for i in id:
group = df[df['id']== i]
polygon = {'type': 'Polygon', 'coordinates': group['geometry']}
polygon['poly'] = polygon
polygons = geopandas.concat([polygon,polygons])
It creates a polygon but when I assign the new variable poly
it says
ValueError: Length of values does not match length of index"
which makes sense since it is still just a list of coordinates and not an actual polygon object. Does anyone know how to make this an actual polygon object that I can add to a column on a geopandas df
?
Thanks in advance :)
I have achieved something similar with the groupby
function. Assuming your points are actually Shapely Point
objects, and are sorted in the right order, you can try something like this.
import pandas as pd
import geopandas as gp
from shapely.geometry import Point, Polygon
# Initialize a test GeoDataFrame where geometry is a list of points
df = gp.GeoDataFrame( [['box', Point(1, 0)],
['box', Point(1, 1)],
['box', Point(2,2)],
['box', Point(1,2)],
['triangle', Point(1, 1)],
['triangle', Point(2,2)],
['triangle', Point(3,1)]],
columns = ['shape_id', 'geometry'],
geometry='geometry')
# Extract the coordinates from the Point object
df['geometry'] = df['geometry'].apply(lambda x: x.coords[0])
# Group by shape ID
# 1. Get all of the coordinates for that ID as a list
# 2. Convert that list to a Polygon
df = df.groupby('shape_id')['geometry'].apply(lambda x: Polygon(x.tolist())).reset_index()
# Declare the result as a new a GeoDataFrame
df = gp.GeoDataFrame(df, geometry = 'geometry')
df.plot()