[Abridged] Detecting cosmic ray hits (cosmics) in fiber-fed IFS data of single exposures is a challenging task, because of the complex signal recorded by IFS instruments. Existing detection algorithms are commonly found to be unreliable in the case of IFS data and the optimal parameter settings are usually unknown a-priori for a given dataset. The CALIFA survey generates hundreds of IFS datasets for which a reliable and robust detection algorithm for cosmics is required as an important part of the fully automatic CALIFA data reduction pipeline. We developed a novel algorithm, PyCosmic, which combines the edge-detection algorithm of L.A.Cosmic with a point-spread function convolution scheme. We generated mock data to compute the efficiency of different algorithms for a wide range of characteristic fibre-fed IFS datasets using the PMAS and VIMOS IFS instruments as representative cases. PyCosmic is the only algorithm that achieves an acceptable detection performance for CALIFA data. We find that PyCosmic is the most robust tool with a detection rate of >~90% and a false detection rate <5% for any of the tested IFS data. It has one less free parameter than the L.A.Cosmic algorithm. Only for strongly undersampled IFS data does L.A.Cosmic exceed the performance of PyCosmic by a few per cent. DCR never reaches the efficiency of the other two algorithms and should only be used if computational speed is a concern. Thus, PyCosmic appears to be the most versatile cosmics detection algorithm for IFS data. It is implemented in the new CALIFA data reduction pipeline as well as in recen
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