Avoid using np.take(x,x); use mode='clip'
DOC: document how to run slow tests
TST: sparse: add a benchmark for sparse __setitem__ and __getitem__
DOC: stats: mention deprecations in the release notes
BUG/ENH: stats: make frozen distributions separate instances

Previously, rv_frozen instances held a reference to a global distribution
instance.
MAINT: stats: use/mention stats.XXX.sf methods instead of special.YYY
ENH support CSR/CSC __setitem__ where duplicates interfere

Also use izip instead of zip
ENH much more efficient sparse min/max with axis
ENH function to return nnz per row/column in some sparse matrices
TST/FIX better test coverage for minmax, and handle 0-size
TST ndarray-compatible handling of zero-size arrays in max/min
ENH: added C sqeuclidean_distance, pdist_sqeuclidean and cdist_sqeuclidean
ENH: added wrappers for cdist_sqeuclidean and pdist_sqeuclidean
ENH: pdist and cdist now call faster C functions for sqeuclidean metric
ENH still faster min/max using ufunc.reduceat

H/T @jaimefrio
DOC add comments to min/max
FIX for Numpy 1.5 cannot use where
ENH factor out minor-axis reduce operation and reuse for sum
Merge pull request #3252 from emanuele/fast_sqeuclidean

ENH: spatial: Fast sqeuclidean

Current scipy.spatial.distance.cdist() and scipy.spatial.distance.pdist() have a very slow implementation for metric='sqeuclidean'. The current scipy implementation consists of computing the distances with metric='euclidean' (which involves sqrt()) and then - literally - "**2". Since both sqrt() and **2 are useless for 'sqeuclidean' and expensive, this PR means to enhance the speed of computation by avoiding useless computations. This PR adds low-level C functions for 'sqeuclidean' computation, adds the wrappers and the necessary Python code to call them from pdist() and cdist(). Note that there is no change in the API or in the docs. The improved cdist() and pdist() of this PR - when metric='sqeuclidean' - are at least 2x faster than current scipy's ones.
Merge pull request #3247 from jnothman/nnz_axis

ENH: sparse: function to return nnz per row/column in some sparse matrices