Using pandas to convert easting and northing to lat and lon given multiple epsg codes

Question

I've gathered information from many separate excel files into a dataframe that contains East and North data given in 10 different coordinate systems (EPSG codes). I can use pyproj to convert one given data point with (epgs_code, east, north) into (lat, lon) for plotting in plotly express, for example.

To add the latitud and longitud data to my dataframe I created a function convert_to_lonlat(epsg, east, north) and I am attempting to apply it to the dataframe to get the new columns, as shown in the code below.

I created a function that takes in the epsg code, east and north, and returns lon and lat values:

def convert_to_lonlat(epsg, east, north):
    import pyproj
    # Define the projection
    epsg_proj = pyproj.CRS.from_epsg(int(epsg))

    # Create a transformation function using the projection
    transform = pyproj.Transformer.from_crs(epsg_proj, 'epsg:4326', always_xy=True)

    # Transform the coordinates
    lon, lat = transform.transform(north, east)

    return lon, lat

A simple test dataframe would be:

geo = pd.DataFrame({'East':[1e6], 'North':[1e6], 'EPSG':[3116]})

I'm trying to use the .apply() method to create two columns in a dataframe that contains data in many epsg codes, as:

geo[['lat','lon']] = geo.apply(
    lambda row: convert_to_lonlat(
        row['EPSG'],
        row['East'],
        row['North'],
        axis=1,
        result_type='expand',
        )
)

which incidentally was suggested by ChatGPT.

However, when I run the code on my dfataframe I get a KeyError:'EPSG' message. Any suggestions as of how this could be fixed? Thank you in advance!

For reference, the full answer from ChatGPT to the query: "use .apply() in pandas using a function that takes 3 arguments and returns two values to create two new columns" was:

import pandas as pd

# Define the function that takes three arguments and returns two values
def my_function(arg1, arg2, arg3):
    result1 = arg1 + arg2 + arg3
    result2 = arg1 * arg2 * arg3
    return result1, result2

# Create a sample dataframe
df = pd.DataFrame({'arg1': [1, 2, 3, 4, 5],
                   'arg2': [10, 20, 30, 40, 50],
                   'arg3': [100, 200, 300, 400, 500]})

# Use the apply() function to create two new columns using the my_function
df[['result1', 'result2']] = df.apply(lambda row: my_function(row['arg1'], row['arg2'], row['arg3']), axis=1, result_type='expand')

# Print the resulting dataframe
print(df)

which works, printing out:

   arg1  arg2  arg3  result1  result2
0     1    10   100      111     1000
1     2    20   200      222     8000
2     3    30   300      333    27000
3     4    40   400      444    64000
4     5    50   500      555   125000