Plotting Pandas Dataframe data using Folium GeoJson

I am trying to plot the amount of times a satellite goes over a certain location using Python and a heatmap. I easily generate the satellite data, but I am having issues with displaying it in a nice manner. I am trying to follow this example, as I can use the style function to lower the opacity. I am having some issues replicating this though as it seems that the GeoJson version they were using no longer accepts the same inputs. This is the dataframe I am using:

print(df.head())
     latitude  longitude  countSp                     geometry
0     -57.9      151.1      1.0  POLYGON ((151.05 -57.95, 151.15 -57.95, 151.15...
1     -57.9      151.2      2.0  POLYGON ((151.15 -57.95, 151.25 -57.95, 151.25...
2     -57.8      151.2      1.0  POLYGON ((151.15 -57.84999999999999, 151.25 -5...
3     -57.8      151.3      3.0  POLYGON ((151.25 -57.84999999999999, 151.35 -5...
4     -57.8      151.4      2.0  POLYGON ((151.35 -57.84999999999999, 151.45 -5...

I then call folium through:

hmap = folium.Map(location=[42.5, -80], zoom_start=7, )
colormap_dept = branca.colormap.StepColormap(
        colors=['#00ae53', '#86dc76', '#daf8aa',
            '#ffe6a4', '#ff9a61', '#ee0028'],
        vmin=0,
        vmax=max_amt,
        index=[0, 2, 4, 6, 8, 10, 12])
    
style_func = lambda x: {
        'fillColor': colormap_dept(x['countSp']),
        'color': '',
        'weight': 0.0001,
        'fillOpacity': 0.1
    }

folium.GeoJson(
    df,
    style_function=style_func,
).add_to(hmap)

This is the error I get when I run my code:

ValueError: Cannot render objects with any missing geometries: latitude  longitude  countSp  geometry

I know that I can use the HeatMap plugin from folium in order to get most of this done, but I have found a couple of issues with doing that. First is that I cannot easily generate a legend (though I have been able to work around this). Second is that it is way too opaque, and I am not finding any ways of reducing that. I have tried playing around with the radius, and blur parameters for HeatMap without much change. I think that the fillOpacity of the style_func above is a much better way of making my data translucent.

By the way, I generate the polygon in my df by the following command. So in my dataframe all I need folium to know about is the geometry and countSp (which is the number of times a satellite goes over a certain area - ~10kmx10km square).

    df['geometry'] = df.apply(lambda row: Polygon([(row.longitude-0.05, row.latitude-0.05), 
                                               (row.longitude+0.05, row.latitude-0.05),
                                               (row.longitude+0.05, row.latitude+0.05),
                                               (row.longitude-0.05, row.latitude+0.05)]), axis=1)

Is there a good way of going about this issue?

Solution

Once again, they were looking for a way to express the purpose in a heat map, so I used Plotly's data on airline arrivals and departures to visualize it.

The number of flights to and from the U.S. mainland only was used for the data.

Excluded IATA codes['LIH','HNL','STT','STX','SJU','OGG','KOA']

Draw a straight line on the map from the latitude and longitude of the departure airport to the latitude and longitude of the arrival airport.
Draw a heat map with data on the number of arrivals and departures by airport.
Since we cannot use a discrete colormap, we will create a linear colormap and add it.
Embed the heatmap as a layer named Traffic

import pandas as pd

df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df_airports.sort_values('cnt', ascending=False)
df_air = df_airports[['lat','long','cnt']]

df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')

df_flight_paths = df_flight_paths[~df_flight_paths['airport1'].isin(['HNL','STT','SJU','OGG','KOA'])]
df_flight_paths = df_flight_paths[~df_flight_paths['airport2'].isin(['LIH','HNL','STT','STX','SJU'])]

df_flight_paths = df_flight_paths[['start_lat', 'start_lon', 'end_lat', 'end_lon', 'cnt']]

import folium
from folium.plugins import HeatMap
import branca.colormap as cm
from collections import defaultdict

steps=10
colormap = cm.linear.YlGnBu_09.scale(0, 1).to_step(steps)
gradient_map=defaultdict(dict)

for i in range(steps):
    gradient_map[1/steps*i] = colormap.rgb_hex_str(1/steps*i)
    
m = folium.Map(location=[32.500, -97.500], zoom_start=4, tiles="cartodbpositron")

data = []
for idx,row in df_flight_paths.iterrows():
    folium.PolyLine([[row.start_lat, row.start_lon], [row.end_lat, row.end_lon]], weight=2, color="red", opacity=0.4
).add_to(m)
    
HeatMap(
    df_air.values,
    gradient=gradient_map,
    name='Traffic',
    mini_opacity=0.1,
    radius=15,
    blur=5
).add_to(m)

folium.LayerControl().add_to(m)
colormap.add_to(m)
m