How to convert lat / lon points to pandas and see if they fall into some boundary polygons?

Question

I have a Pandas dataframe dflike this:

 id   lat  lon
 jhg  2.7  3.5
 ytr  3.1  3.5
 ...

I also have a Geopandas data file polywith some polygons. Now I would only like to build points in dfwhich are inside some polygon. So I would have to do something like poly.intersects(p), where pis Shapely Point. But I'm doing something wrong

from shapely.geometry import Point
for index, row in df.iterrows():
    t = poly.intersects(Point(row.lon, row.lat))

What would be the best way to transfer data with lat / lon dots and overlay them on poly? Note that I can define the min / max lat / lon range, but also prints points outside poly, but inside the (larger) bounding box.

Answer 1

:

import pandas as pd
from shapely.geometry import box
import matplotlib.pyplot as plt

from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
from shapely.geometry import Point
import seaborn as sns
import numpy as np

# some pretend data
data = {'lat':[2.7,3.5,1.4,2.3,.9,1.9], 'lon':[1.2,.9,1.9,2.2,3,1.1]}
df = pd.DataFrame(data)

# the 'bounding' polygon
poly = box(1,1,2,2)
patches  = PatchCollection([Polygon(poly.exterior)], facecolor='red', linewidth=.5, alpha=.5)


# plot the bounding box 
fig, ax = sns.plt.subplots(1, figsize=(4,4))
ax.add_collection(patches, autolim=True)

# plot the lat/lon points
df.plot(x='lat',y='lon', kind='scatter',ax=ax)
plt.show()

:

:

#probably more efficient ways to do this, but this works
mask = [poly.intersects(Point(lat,lon)) for lat,lon in zip(df.lat,df.lon)]
df = df[mask]

# make new plot (must make a new 'patch' object)
patches1  = PatchCollection([Polygon(poly.exterior)], facecolor='red', linewidth=.5, alpha=.5)
fig1, ax1 = sns.plt.subplots(1, figsize=(4,4))
ax1.add_collection(patches1, autolim=True)

# make the axis bounds the same
ax1.set_xlim(ax.get_xlim())
ax1.set_ylim(ax.get_ylim())

# plot the lat/lon points
df.plot(x='lat',y='lon', kind='scatter',ax=ax1)
plt.show()

.

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ax.set_xlim((np.min(poly.exterior.xy[0]),np.max(poly.exterior.xy[0])) )
ax.set_ylim((np.min(poly.exterior.xy[1]),np.max(poly.exterior.xy[1])) )

:

Answer 2

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spatial_index = gdf.sindex
possible_matches_index = list(spatial_index.intersection(polygon.bounds))
possible_matches = gdf.iloc[possible_matches_index]
precise_matches = possible_matches[possible_matches.intersects(polygon)]

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