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import pandas as pd
from geopy.distance import geodesic
# Function to calculate distances while preserving all original columns
def calculate_distances(
df1: pd.DataFrame,
df2: pd.DataFrame,
code_col1,
lat_col1,
long_col1,
code_col2,
lat_col2,
long_col2,
):
distances = []
for _, row1 in df1.iterrows():
for _, row2 in df2.iterrows():
coord1 = (row1[lat_col1], row1[long_col1])
coord2 = (row2[lat_col2], row2[long_col2])
distance_km = geodesic(coord1, coord2).kilometers # Compute distance
# Combine all original columns + distance
combined_row = {
**row1.to_dict(), # Keep all columns from Dataset1
**{
f"{col}_Dataset2": row2[col] for col in df2.columns
}, # Keep all columns from Dataset2
"Distance_km": distance_km,
}
distances.append(combined_row)
df_distances = pd.DataFrame(distances)
# Find the closest point for each Point1
df_closest: pd.DataFrame = df_distances.loc[
df_distances.groupby(code_col1)["Distance_km"].idxmin()
]
return df_distances, df_closest
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