The Geopandas DataFrame Coordinates Example

Source

Beginning

Imports

# python
from functools import partial

# pypi
import geopandas
import hvplot.pandas
import matplotlib.pyplot as pyplot
import pandas

# my stuff
from graeae import EmbedHoloviews

Set Up

Embed = partial(EmbedHoloviews, folder_path="files/posts/the-geopandas-dataframe-coordinates-example")

Middle

Longitudes and Latitudes

data = pandas.DataFrame(
    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
     'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
     'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})

geoframe = geopandas.GeoDataFrame(data,
                                  geometry=geopandas.points_from_xy(
                                      data.Longitude, data.Latitude))

print(geoframe.head())

           City    Country  Latitude  Longitude                     geometry
0  Buenos Aires  Argentina    -34.58     -58.66  POINT (-58.66000 -34.58000)
1      Brasilia     Brazil    -15.78     -47.91  POINT (-47.91000 -15.78000)
2      Santiago      Chile    -33.45     -70.66  POINT (-70.66000 -33.45000)
3        Bogota   Colombia      4.60     -74.08    POINT (-74.08000 4.60000)
4       Caracas  Venezuela     10.48     -66.86   POINT (-66.86000 10.48000)

Plot It

# read in the plotting data
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))

# restrict it to South America.
ax = world[world.continent == 'South America'].plot(
    color='white', edgecolor='black')

# We can now plot our ``GeoDataFrame``.
geoframe.plot(ax=ax, color='red')

pyplot.savefig("files/posts/the-geopandas-dataframe-coordinates-example/south_america.png")

nil

With Holoviews

Since this is going to be interactive we can add more to the plot.

print(world.head())

     pop_est      continent                      name iso_a3  gdp_md_est  \
0     920938        Oceania                      Fiji    FJI      8374.0   
1   53950935         Africa                  Tanzania    TZA    150600.0   
2     603253         Africa                 W. Sahara    ESH       906.5   
3   35623680  North America                    Canada    CAN   1674000.0   
4  326625791  North America  United States of America    USA  18560000.0   

                                            geometry  
0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...  
1  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...  
2  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...  
3  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...  
4  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...

Let's add the population estimate to the countries and the city to the points.

world_plot = world[world.continent == "South America"].hvplot(
    color="white",
    x="Longitude",
    y="Latitude",
    legend=False,
    hover_cols=["pop_est", "name"])

# NOTE: the overlay uses the pandas.DataFrame, not the GeoDataFrame
points = data.hvplot.points("Longitude", "Latitude", hover_cols=["City"])
plot = (world_plot * points).opts(
    title="South American Cities",
    width=700,
    height=890,
)
outcome = Embed(plot=plot, file_name="south_america", height_in_pixels=1000)()

print(outcome)

Figure Missing

This was mostly a check to see if I could get it working so I'll have to think about adding more explanations later.

Notes

  • To get matplotlib to work I had to install descartes (pip install descartes)
  • To get hvplot to work I had to install geoviews (pip install geoviews)

Links