Exploring GeoDataFrames
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.
12345678910import geopandas as gpd # Load Natural Earth countries dataset url = "https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_countries.zip" gdf = gpd.read_file(url) # Filter countries with population greater than 100 million large_countries = gdf[gdf["POP_EST"] > 100_000_000] print(large_countries[["NAME", "POP_EST"]])
12345678910111213import geopandas as gpd # Load Natural Earth countries dataset url = "https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_countries.zip" gdf = gpd.read_file(url) # Reproject to an equal-area CRS (World Mollweide) gdf = gdf.to_crs("ESRI:54009") # Calculate area in square kilometers gdf["area_km2"] = gdf.geometry.area / 1e6 print(gdf[["NAME", "area_km2"]].head())
Display the first few rows of the GeoDataFrame to quickly inspect data;
Create a basic map visualization of the geometries in the GeoDataFrame;
Generate summary statistics for all numeric columns, including attribute data;
Show the coordinate reference system used by the GeoDataFrame;
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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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.
12345678910import geopandas as gpd # Load Natural Earth countries dataset url = "https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_countries.zip" gdf = gpd.read_file(url) # Filter countries with population greater than 100 million large_countries = gdf[gdf["POP_EST"] > 100_000_000] print(large_countries[["NAME", "POP_EST"]])
12345678910111213import geopandas as gpd # Load Natural Earth countries dataset url = "https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_countries.zip" gdf = gpd.read_file(url) # Reproject to an equal-area CRS (World Mollweide) gdf = gdf.to_crs("ESRI:54009") # Calculate area in square kilometers gdf["area_km2"] = gdf.geometry.area / 1e6 print(gdf[["NAME", "area_km2"]].head())
Display the first few rows of the GeoDataFrame to quickly inspect data;
Create a basic map visualization of the geometries in the GeoDataFrame;
Generate summary statistics for all numeric columns, including attribute data;
Show the coordinate reference system used by the GeoDataFrame;
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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