Category-encoders

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* Onehot transform returns same columns always
* Missing value and unknown handling now configurable in all relevant encoders

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* Added more sophisticated missing value or unknown category handling to ordinal
* Passing through missing value config from onehot into ordinal
* Onehot will return an extra column when unknown categories are passed in if impute is used.
* Added BaseNEncoder to allow for more flexible alternatives to ordinal, onehot and binary.

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* Full support for numpy arrays as input, not just dataframes.

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* All encoders handle missing values and are tested for their handling
* Created a onehot encoder that follows the same conventions as the rest of the library instead of using sklearns.
* Did some basic benchmarking for data compression and memory usage, made some performance improvements
* Changed all docstrings to numpy style and added more documentation
* Moved all logic methods into staticmethods of the transformer classes themselves.
* Added more detailed checks for type and shape of input data in fit and transform
* Support input as list of lists, alongside numpy arrays and pandas dataframes.

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* Better handling for missing values in hashing encoder

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* Testing enhancements
* Hash type in hashing encoder now defaults to md5 using hashlib, but can be set to any valid hashlib hash