NEOCC has just released esaneocc, a Python library that provides a high-level programmatic interface to the data published through our web portal. It can be installed with a single command: pip3 install esaneocc

The library is a convenient wrapper around the existing Automated Data Access endpoints, a set of HTTPS GET calls that have been available for some time and allow direct, unauthenticated retrieval of NEOCC data in plain-text format. Where those endpoints return raw fixed-width or pipe-delimited text, esaneocc parses and delivers the same content as ready-to-use pandas DataFrames and structured Python objects, removing the need for users to handle parsing, URL encoding, or format conversions themselves.

The library exposes two main entry points. query_list() gives access to catalogue-level data: the full list of near-Earth asteroids maintened by NEOCC, the daily updated risk list with Palermo and Torino Scale values, the upcoming and recent close approach tables, the close encounter list, the impacted objects catalogue, and the NEA orbital element catalogues in both current-epoch and middle-arc variants. query_object() provides per-object access to summary data, physical properties, orbital elements and covariance matrices, astrometric observation records, computed ephemerides, close approach tables, and virtual impactor lists. Full documentation for the library is self-contained within the package itself: all functions include inline docstrings describing parameters and return types. Additional details, along with ready-to-use code snippets for most of the available call, can be found on the dedicated Python Interface page.

The underlying HTTP endpoints remain the primary interface and are documented in full on the Automated Data Access page, which also includes the raw output formats and URL parameter reference. The Python library described here is intended to lower the barrier of entry for users working in scientific Python environments and should be particularly useful for researchers wishing to incorporate NEOCC data into pipelines, notebooks, or automated workflows.
 

Example Python script querying the NEOCC risk list, selecting the largest object, and retrieving its ephemerides for the next 7 days.