Add input_df init argument to pass df/series to transformers

[dukebody]

Work for #60.

Passing Series/DataFrames to the transformers

By default the transformers are passed a numpy array of the selected columns
as input. This is because sklearn transformers are historically designed to
work with numpy arrays, not with pandas dataframes, even though their basic
indexing interfaces are similar.

However we can pass a dataframe/series to the transformers to handle custom
cases initializing the dataframe mapper with ``input_df=True`::

    >>> from sklearn.base import TransformerMixin
    >>> class DateEncoder(TransformerMixin):
    ...    def fit(self, X, y=None):
    ...        return self
    ...
    ...    def transform(self, X):
    ...        dt = X.dt
    ...        return pd.concat([dt.year, dt.month, dt.day], axis=1)
    >>> dates_df = pd.DataFrame(
    ...     {'dates': pd.date_range('2015-10-30', '2015-11-02')})
    >>> mapper_dates = DataFrameMapper([
    ...     ('dates', DateEncoder())
    ... ], input_df=True)
    >>> mapper_dates.fit_transform(dates_df)
    array([[2015,   10,   30],
           [2015,   10,   31],
           [2015,   11,    1],
           [2015,   11,    2]])

[dukebody]

@jph00, @hshteingart can you check this and confirm it works correctly with your use-cases?

[jph00]

Thanks! :) I don't have to time study it in depth right now, but it looks pretty good to me.