Joris Van den Bossche

GeoPandas is one of the core packages in the Python ecosystem to work with geospatial vector data, combining the power of a whole ecosystem of open source geo tools. This talk with given an overview of recent developments in the GeoPandas project and the broader geospatial community.

The geometries in GeoPandas, using the Shapely library, are assumed to be in projected coordinates on a flat plane. While this approximation is often just fine, for global data this runs into its limitations.

Apache Arrow has become a de-facto standard for efficient in-memory columnar data representation. Beyond the standardized and language-independent columnar memory format for tabular data, the Apache Arrow project also has a growing set of supplementary specifications and language implementations.

Apache Arrow has become a de-facto standard for efficient in-memory columnar data representation. You might have heard about Arrow or using Arrow, but do you understand the format and why it’s so useful?

Last year, the pandas community adopted a new process for making significant changes to the library: the Pandas Enhancement Proposals, aka PDEPs. In the meantime, several of those proposals have been proposed and discussed, and some already accepted.

The Apache Arrow (https://arrow.apache.org/) project specifies a standardized language-independent columnar memory format. It enables shared computational libraries, zero-copy shared memory, streaming messaging and interprocess communication without serialization overhead, etc.