GeoPandas transform latitude/longitude coordinates to UTM coordinates

Question

I have a shapefile which I read with GeoPandas, this shapefile has lat/long coordinates and I want to transform it to UTM coordinates. I tried this:

shapefile = gpd.read_file('example.shp') #CRS = epsg:4326
shapefile.to_crs("+proj=utm +ellps=WGS84 +datum=WGS84 +units=m +no_defs",inplace=True)

But it doesn't transform my coordinates correctly. I think it is because I have to explicitly put in the code the UTM zone with the number and letter which can vary depending on the shapefile I read.

So for calculating the letter and number I tried this:

import utm
utmZones = set()
for p in shapefile.geometry:
    utmZones.add(utm.from_latlon(p.centroid.y,p.centroid.x)[2:4])
print(utmZones)

{(20, 'H'), (20, 'J'), (21, 'H')}

If the polygons fall in three different adjacent UTM zones is there any problem if I choose the UTM zone which is in the middle or should I create three different shapefiles one for each UTM zone?

Answer 1

"choose the UTM zone which is in the middle or should I create three different shapefiles one for each UTM zone?". It depends on your demands of accuracy.

The further from the center meridian you move in the more distorted your data will become. See: https://gisgeography.com/utm-universal-transverse-mercator-projection/

You can use .estimate_utm_crs to find a suitable utm crs, reproject and save to file.

I have a data frame with three countries in it: Spain, Italy and Turkey. They are all in different UTM zones:

import geopandas as gpd
import os
df = gpd.read_file(r"/home/bera/Desktop/GIStest/spain_italy_turkey.geojson")
#print(df.crs)
#EPSG:4326

#I have a unique ID column called NAME
for name, subframe in df.groupby("NAME"):
    print(name)
    print(subframe.estimate_utm_crs(datum_name='WGS 84')) #Find its utm zone
    # Italy
    # EPSG:32633
    # Spain
    # EPSG:32629
    # Turkey
    # EPSG:32636
    
#For each country, create a new shapefile
out_folder = r"/home/bera/Desktop/GIStest/utm_converted/"
for name, subframe in df.groupby("NAME"):
    subframe = subframe.to_crs(subframe.estimate_utm_crs(datum_name='WGS 84')) #Reproject
    
    #Save to file
    out_file = os.path.join(out_folder, f"{name}_reprojected.shp")
    subframe.to_file(out_file)