Mistake with lambda function applied on geodataframe's row

Question

I trying to transform a 3D geodataframe of points in 2D. So I've develped the function below:

def point3D_to_2D(point3D_wkt: shapely.geometry.point.Point) -> shapely.geometry.point.Point:
    print(point3D_wkt)
    point2D_wkt = transform(lambda x, y, z=None: (x, y), point3D_wkt).wkt
    print(point2D_wkt)

    return point2D_wkt

My geodataframe's name is geom(I've a lot of fantasy!) and the geometry column's name is geometry. Using the code below:

geom['geometry'] = geom.apply(lambda row : point3D_to_2D(row.geometry))

I see this error:

AttributeError: 'Series' object has no attribute 'geometry'

But using type(geom) I see:

geopandas.geodataframe.GeoDataFrame

Answer 1

convert 3D geometry to 2D

• it's a simple case of not using last (3rd) z coordinate of point
• no need to work with WKT strings
• an issue with your code is you are using apply() to GeoDataFrame, plus not taking care of axis=1 to that it will be row by row
• far simpler to work with just GeoSeries so no need to consider axis of data frame

gdf["geometry"].apply(lambda p: shapely.geometry.Point(p.coords[0][:-1]))

data source 3D points

• head(10) output shows it's 3D geometry (POINT Z)

import geopandas as gpd
import pandas as pd
import shapely
df = pd.read_html("https://en.wikipedia.org/wiki/List_of_cities_by_elevation")[1]

df["Lat"] = pd.to_numeric(df["Latitude"].replace({"N":"+","S":"-"}, regex=True), errors="coerce")
df["Lon"] = pd.to_numeric(df["Longitude"].replace({"E":"+","W":"-"}, regex=True), errors="coerce")
df["Elevation (m)"] = pd.to_numeric(df["Elevation (m)"], errors="coerce")
df = df.dropna(subset=["Lat","Lon","Elevation (m)"])

gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df["Lon"], df["Lat"], df["Elevation (m)"]), crs="epsg:4386")
gdf.head(10)

	Country/Territory	City Name/s	Continental Region	Latitude	Longitude	Population	Elevation (m)	Lat	Lon	geometry
0	Nepal	Pokhara	Asia	N28.2096	E83.9856	523000	822	28.2096	83.9856	POINT Z (83.9856 28.2096 822)
1	South Africa	Bloemfontein	Africa	S29.116667	E026.216667	747431	1395	-29.1167	26.2167	POINT Z (26.216667 -29.116667 1395)
2	China	Shanghai	Asia	N31.2304	E121.4737	2.632e+07	122	31.2304	121.474	POINT Z (121.4737 31.2304 122)
3	Nepal	Butwal	Asia	N27.6866	E83.4323	120982	150	27.6866	83.4323	POINT Z (83.4323 27.6866 150)
4	Italy	Milan	Europe	N45.4625	E9.186389	1.37869e+06	122	45.4625	9.18639	POINT Z (9.186389 45.4625 122)
5	Kazakhstan	Pavlodar	Asia	N52.3000	E76.950000	353930	123	52.3	76.95	POINT Z (76.95 52.3 123)
6	South Africa	Pretoria	Africa	S25.746111	E028.188056	2.92149e+06	1339	-25.7461	28.1881	POINT Z (28.188056 -25.746111 1339)
7	Albania	Tirana	Europe	N41.3317	E019.8172	557422	110	41.3317	19.8172	POINT Z (19.8172 41.3317 110)
8	Austria	Vienna	Europe	N48.2092	E016.3728	1.89906e+06	170	48.2092	16.3728	POINT Z (16.3728 48.2092 170)
9	Belarus	Minsk	Europe	N53.9678	E027.5766	1.98244e+06	198	53.9678	27.5766	POINT Z (27.5766 53.9678 198)