I have a DataFrame that resulted from spatially joining a digital elevation map with a square grid map.

enter image description here

This unexpectedly resulted in duplicate rows where two rows will have the same "geometry" but a different "Elevation" value.

How do I get the median of "Elevation" for each unique "geometry"? I'm new to GeoPandas, so I tried the traditional methods of aggregating a DataFrame, but found that "geometry" cannot be operated with the groupby() function.


I have also tried using the dissolve() function, but I don't think I'm doing it correctly because the number of rows were reduced to just seventy (70) as opposed to the original two thousand (2000) before the spatial join.

mrkna_grid.dissolve(by="Elevation", aggfunc="median")
  • 1
    Do you want to keep the duplicate geometries?
    – BERA
    Nov 5, 2021 at 8:20
  • @BERA no, I just need every unique "geometry" and the median of all its "Elevation" associated with said "geometry"
    – SS-Salt
    Nov 5, 2021 at 8:30

1 Answer 1


Let's assume there is a polygon layer 'layer' with its attribute table, see the image below.


The expected result of median is:

Polygon 1 | 4
Polygon 2 | 7
Polygon 3 | 15

Using one of the following code:

Solution #1 includes Pandas/GeoPandas native DataFrame.median()

import geopandas as gpd

path_to_layer = "C:/Documents/Python Scripts/median/layer.shp"
layer = gpd.read_file(path_to_layer)

layer_ = layer.groupby(layer["geometry"].to_wkt())['Elevation'].median().reset_index(name='MedianElev')
layer_['geometry'] = gpd.GeoSeries.from_wkt(layer_['index'])
layer_.drop(['index'], inplace=True, axis=1)

output = gpd.GeoDataFrame(layer_, geometry='geometry')
output = output.set_crs(layer.crs)

Solution #2 includes Python's statistics.median()

import geopandas as gpd
from statistics import median

path_to_layer = "C:/Documents/Python Scripts/median/layer.shp"
layer = gpd.read_file(path_to_layer)

layer['geom_wkt'] = layer['geometry'].to_wkt()

layer_ = layer.groupby('geom_wkt')['Elevation'].apply(list).reset_index(name='ElevationList')
layer_['median'] = layer_['ElevationList'].apply(lambda x: median(x))
layer_['geometry'] = gpd.GeoSeries.from_wkt(layer_['geom_wkt'])
layer_.drop(['geom_wkt', 'ElevationList'], inplace=True, axis=1)

output = gpd.GeoDataFrame(layer_, geometry='geometry')
output = output.set_crs(layer.crs)

it is possible to achieve the following output:



  • Thank you. One question though: the code above returns a dataframe that only has "geometry" and "Elevation". How would you hypothetically retain other attributes?
    – SS-Salt
    Nov 5, 2021 at 13:00
  • I will probably do the join on e.g. the "geom_wkt" attribute. What kind of attributes would you like to retain? Can you share a piece of your data?
    – Taras
    Nov 5, 2021 at 13:02
  • The full picture of the DataFrame in the OP has attributes X, Y and Z (for brevity) alongside the shown "geometry" and "Elevation".
    – SS-Salt
    Nov 5, 2021 at 13:13

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