geopandas plotting - Identify locations that fall outside of the map - plot

Question

I have a shapefile that shows the map of Pakistan at district level. I also have a geodataframe that has information about polling stations in Pakistan.
I have mapped the geodataframe on to the shapefile, but noticed that some lat/lon values from the geodataframe are wrong i.e. they lie outside Pakistan.
I want to identify which polling stations these are. (I want to select those rows from the geodataframe) Is there a way to do this?
Please see below for reference - the black dots indicate polling stations, and the colourful map is the map of Pakistan at district levels:
image_pakistan_map_pollingstations
edit:
So I'm trying this and it seems to work, however it's taking a very long time to run (been running it for 5+ hrs now) - for reference, the geodataframe has about 50,000 rows and it's called ours_NA_gdf.
for i in range(len(ours_NA_gdf)):
if ours_NA_gdf['geometry'][i].within(pakistan['geometry'][0]):
ours_NA_gdf.at[i, 'loc_validity'] = 'T'
else:
ours_NA_gdf.at[i, 'loc_validity'] = 'F'
ours_NA_gdf[ours_NA_gdf['loc_validity']=='F']

Answer 1

I suspect that the geometries of Pakistan you use are the problem. They are too complex and detailed to use. In your use-case, simple geometry provided by naturalearth_lowres should give better performance. Here I provide a runnable code that demonstrates the use of simple Pakistan geometry to perform contains() operations, and assign properties color of points to plot on the map.
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from cartopy import crs as ccrs
# create a geoDataFrame of points locations across Pakistan areas
pp = 40
lons = np.linspace(60, 80, pp)
lats = np.linspace(22, 39, pp)
# create point geometry
# points will be plotted across Pakistan in red (outside) and green (inside)
points = [Point(xy) for xy in zip(lons, lats)]
# create a dataframe of 3 columns
mydf = pd.DataFrame({'longitude': lons, 'latitude': lats, 'point': points})
# manipulate dataframe geometry
gdf = mydf.drop(['longitude', 'latitude'], axis=1)
gdf = gpd.GeoDataFrame(gdf, crs="EPSG:4326", geometry=gdf.point)
fig, ax = plt.subplots(figsize=(6,7), subplot_kw={'projection': ccrs.PlateCarree()})
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
parki = world[(world.name == "Pakistan")] #take a country of interest
# grab the geometry of Pakistan
# can .simplify() it if need be
pg = parki['geometry']
newcol = []
for index, row in gdf.iterrows(): # Looping over all points
res = pg.contains( row.geometry).values[0]
newcol.append(res)
# add a new column ('insideQ') to the geodataframe
gdf['insideQ'] = newcol
# add a new column ('color') to the geodataframe
gdf.loc[:, 'color'] = 'green' #set color='green'
# this set color='red' to selected rows
gdf.loc[gdf['insideQ']==False, 'color'] = 'red'
# plot Pakistan
ax.add_geometries(parki['geometry'], crs=ccrs.PlateCarree(), color='lightpink', label='Pakistan')
# plot all points features of `gdf`
gdf.plot(ax=ax, zorder=20, color=gdf.color)
ax.set_extent([60, 80, 22, 39]) #zoomed-in to Pakistan
LegendElement = [
mpatches.Patch(color='lightpink', label='Pakistan')
]
ax.legend(handles = LegendElement, loc='best')
plt.show()
The output plot: