Impara Exploring GeoDataFrames | Spatial Operations and Map Processing

A GeoDataFrame is a specialized data structure that allows you to work with both geometric information and associated attribute data in a tabular format. It extends the familiar DataFrame concept by adding a geometry column, which stores geometric shapes such as points, lines, or polygons. Alongside geometry, each row can contain multiple attribute columns that describe properties of each spatial feature, such as name, population, or land use type. This structure enables you to query, analyze, and visualize geospatial data efficiently.


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import geopandas as gpd

# Load a sample GeoDataFrame of world countries
gdf = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))

# Filter countries with population greater than 100 million
large_countries = gdf[gdf["pop_est"] > 100_000_000]

print(large_countries[["name", "pop_est"]])


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import geopandas as gpd

# Load the sample GeoDataFrame again
gdf = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))

# Calculate the area of each country (in square degrees)
gdf["area"] = gdf["geometry"].area

print(gdf[["name", "area"]].head())

head

Display the first few rows of the GeoDataFrame to quickly inspect data;

plot

Create a basic map visualization of the geometries in the GeoDataFrame;

describe

Generate summary statistics for all numeric columns, including attribute data;

crs

Show the coordinate reference system used by the GeoDataFrame;

dissolve

Combine geometries and summarize attributes based on a group column.

1. What is the 'geometry' column in a GeoDataFrame?

2. Which method would you use to visualize a GeoDataFrame?

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Sezione 2. Capitolo 1

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Suggested prompts:

What is the difference between a DataFrame and a GeoDataFrame?

How can I visualize the geometries in a GeoDataFrame?

Can you explain what types of geometry objects can be stored in the geometry column?

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              123456789
            
import geopandas as gpd

# Load a sample GeoDataFrame of world countries
gdf = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))

# Filter countries with population greater than 100 million
large_countries = gdf[gdf["pop_est"] > 100_000_000]

print(large_countries[["name", "pop_est"]])


              123456789
            
import geopandas as gpd

# Load the sample GeoDataFrame again
gdf = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))

# Calculate the area of each country (in square degrees)
gdf["area"] = gdf["geometry"].area

print(gdf[["name", "area"]].head())