Histogrammar

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* fix of get_sub_hist() when Bin histogram is filled only with nans.

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* Support for decimal datetype in pandas and spark.

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* fix of spark df timestamp datatype detection (59)
* fix for invalid bin_edges for SparselyBin histogram (60)

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* Fix for machine-level rounding error, which can show up on in num_bins() call of Bin histogram.
* supersedes broken v1.0.29

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* Multiple performance updates, to Bin, SparselyBin and Categorize histograms.
* SparselyBin, Categorize: optimized filling with 1-d and 2-d numpy arrays
* Bin, SparselyBin, Categorize: (fast) numpy arrays for bin-centers and bin-labels.
* Count: new, fast filling option when float weight is known.
* util.py: faster get_datatype() and get_ndim() functions.

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* Multiple performance updates, thanks to Simon Brugman.
* Use pandas functions to infer datatypes and return numpy arrays.
* Turn of unnecessary specialize function (slow) for Count objects.