Extracellular recordings with multi-electrode arrays is one of the basic tools of con-temporary neuroscience. These recordings are mostly used to monitor the activities, understood as sequences of emitted action potentials, of many individual neurons. But the raw data produced by extracellular recordings are most commonly a mixture of activities from several neurons. In order to get the activities of the individual con-tributing neurons, a pre-processing step called spike sorting is required. We present here a pure Python implementation of a well tested spike sorting procedure. The latter was designed in a modular way in order to favour a smooth transition from an interac-tive sorting, for instance with IPython, to an automatic one. Surprisingly enough—or sadly enough, depending on one's view point—, recoding our now 15 years old pro-cedure into Python was the occasion of major methodological improvements.