  Astronomy is entering the era of large surveys of the variable sky such as
Zwicky Transient Facility (ZTF) and the forthcoming Legacy Survey of Space and
Time (LSST) which are intended to produce up to a million alerts per night.
Such an amount of photometric data requires efficient light-curve
pre-processing algorithms for the purposes of subsequent data quality cuts,
classification, and characterization analysis. In this work, we present the new
library "light-curve" for Python and Rust, which is intended for feature
extraction from light curves of variable astronomical sources. The library is
suitable for machine learning classification problems: it provides a fast
implementation of feature extractors, which outperforms other public available
codes, and consists of dozens features describing shape, magnitude
distribution, and periodic properties of light curves. It includes not only
features which had been shown to provide a high performance in classification
tasks, but also new features we developed to improve classification quality of
selected types of objects. The "light-curve" library is currently used by the
ANTARES, AMPEL, and Fink broker systems for analyzing the ZTF alert stream, and
has been selected for use with the LSST.
