  Data gridding is a common task in astronomy and many other science
disciplines. It refers to the resampling of irregularly sampled data to a
regular grid. We present cygrid, a library module for the general purpose
programming language Python. Cygrid can be used to resample data to any
collection of target coordinates, although its typical application involves
FITS maps or data cubes. The FITS world coordinate system standard is
supported. The regridding algorithm is based on the convolution of the original
samples with a kernel of arbitrary shape. We introduce a lookup table scheme
that allows us to parallelize the gridding and combine it with the HEALPix
tessellation of the sphere for fast neighbor searches. We show that for $n$
input data points, cygrids runtime scales between O(n) and O(n log n) and
analyze the performance gain that is achieved using multiple CPU cores. We also
compare the gridding speed with other techniques, such as nearest-neighbor, and
linear and cubic spline interpolation. Cygrid is a very fast and versatile
gridding library that significantly outperforms other third-party Python
modules, such as the linear and cubic spline interpolation provided by SciPy.
