Spatial join list lat,lon and multipolygon Python

Question

I have a dataframe with lat, lon columns in WGS84.

enter image description here

I also have a multipolygon layer (GeoJSON, I can also convert it to a SHP) with all countries' boundaries, where the name of the country is in the attribute table.

I would like to add to the dataframe a column where for each lat, lon I'll have the country name.

What would be the efficient way to do that (assuming I have 2000 lat lon pairs)?

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With help from @BERA I've created this function:

import geopandas as gpd
from shapely.geometry import Point
df_countries = gpd.read_file(r"C:\countries.geojson")
def get_countries(df, lat_col, lon_col,df_countries):
 df_latlon = df[[lat_col,lon_col]].copy()
 df_latlon['Coordinates'] = list(zip(df_latlon[lon_col], df_latlon[lat_col]))
 df_latlon['Coordinates'] = df_latlon['Coordinates'].apply(Point)
 df_latlon = gpd.GeoDataFrame(df_latlon, geometry='Coordinates')
 df_latlon = df_latlon.set_crs(epsg=4326)
 df_latlon = gpd.sjoin(df_latlon, df_countries[['CNTRY_NAME','geometry']], how='left')
 return df_latlon

Answer 1

Spatial Join:

import geopandas as gpd
dfpoints = gpd.read_file(r"C:\folder\bs_riks.shp")
dfpolys = gpd.read_file(r"C:\folder\ak_riks.shp")
df = gpd.sjoin(dfpoints, dfpolys, how='left')
#or: df = gpd.sjoin(dfpoints, dfpolys[['CNTRY_NAME','geometry']], how='left') #If you dont want all attributes from the polygons

If you have a pandas df of the coords, create geopandas like this: Creating a GeoDataFrame from a DataFrame with coordinates

• user88484: My lat lon are not in a SHP file, they are in two separate columns in a pandas dataframe
• Bera: Convert the pandas df to a geopandas using the lat longs
• user88484: can you please add it to the code in your answer so I'll mark it as correct?