GeoDataFrame Filtering

Learn how to index and select data in GeoPandas.

Indexing, selecting, and slicing

One of the key strengths of GeoPandas lies in its indexing, selecting, and slicing capabilities, which provide users with efficient and flexible ways to manipulate and analyze geospatial data. It maintains all the capabilities provided by pandas and extends them by adding support for spatial data operations.

In this quick introduction, we'll highlight the key differences and usage scenarios for indexing, selecting, and slicing in GeoPandas:

  • Indexing: Indexing in GeoPandas refers to the process of accessing specific elements, rows, or columns in a GeoDataFrame or GeoSeries based on their position or labels. It can be achieved using the familiar square bracket notation (e.g., gdf[‘column_name’]) or through the .loc[] and .iloc[] methods. The .loc[] method allows us to access data based on labels, while the .iloc[] method operates on integer-based positions.

  • Selecting: Selecting in GeoPandas involves filtering and returning a subset of the data based on certain conditions or criteria. This can be achieved through boolean indexing, which involves applying a boolean mask to select rows or columns that meet the specified condition. For example, we could select rows from a GeoDataFrame where a specific column has values greater than a certain threshold.

  • Slicing: Slicing in GeoPandas is similar to slicing in standard Python lists or pandas DataFrames. It refers to the process of extracting a continuous portion of rows or columns from a GeoDataFrame or GeoSeries based on their position. We can achieve slicing using the colon notation (e.g., gdf[start:end]) or with the .iloc[] method, which can take a range of integer-based positions as input.

While there are some similarities among these features, the key differences lie in their specific use cases. Indexing is primarily used for accessing individual elements, rows, or columns, whereas selecting and slicing are used for extracting subsets of data based on conditions or position ranges, respectively. By understanding these differences, we can effectively manipulate and analyze our geospatial data using GeoPandas.

Tabular filtering

For this example, we'll use a simple Populated Places dataset. This layer will bring the 243 most populated cities in the world with a population estimation obtained from the LandScan project. We can get the layer loaded into a GeoDataFrame through the following command:

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