Support serializing numpy and pandas types

[sethmlarson]

This PR attempts to import numpy and pandas and if either library is found adds to the list of types that the default JSONSerializer supports. Numpy adds the integers, floats, boolean, ndarray, and datetime. Pandas adds support for Series, Timestamp, and NA -> None. Am I missing any important types that can be safely serialized to JSON?

Notably I left out DataFrame and numpy.nan. NaN is already handled by JSON and doesn't have semantics for Elasticsearch (at least I don't think it does?) and DateFrame seemed a bit too heavy to support natively? Better for users to call DataFrame.to_json() themselves?

Also wanted to confirm my thinking that it is appropriate to support Series and ndarray? Or is that also too presumptive of what a user wants from the library?

Closes #1178
Closes elastic/eland#142

[stevedodson]

LGTM

[Winterflower]

I'm definitely not the expert here (so I might asking the wrong question here), but I've been recently working a bit with pandas.dtypes and I'm wondering, how will this serialiser handle the types of the elements inside a pd.Series or a pd.DataFrame. Usually these elements are various numerical types, so probably handled by the Numpy converters, but sometimes a user can set these dtypes to pandas specific stuff like category. For ex. I've recently done something like this in my Jupyter notebook

# finally we will correct the mappings on the remaining columns 

mappings = {'carat': 'float64',
           'cut': 'category',
           'color': 'category',
           'depth':'float64',
           'table':'float64',
           'price': 'float64',
           'x':'float64',
           'y':'float64',
           'z':'float64'}

df_cleaned = df_cleaned.astype(mappings)

How would the serialisation handle category or some of the other pandas specific dtypes in a Series (here is a list of some more exotic dtypes https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#dtypes)?

[sethmlarson]

Category is an interesting one, we'd lose the "categorical" aspect of the value if we serialize to a string but maybe that's fine? Maintaining the categorical aspect would require config on the mapping but as long as that's done it'd be a solution, so maybe we do category -> str?