def featurize(equity_data, n_sessions, **kwargs):
    """
    Generate a raw (unnormalized) feature set from the input data.
    The value at `column` on the given date is taken
    as a feature, and each row contains values for n_sessions

    Parameters
    -----------
    equity_data : DataFrame
        data from which to generate features

    n_sessions : int
        number of sessions to use as features

    selection : str, default: 'Adj Close'
        column of `equity_data` from which to generate features.

    columns : list, default: ``map(str, range((-n_sessions + 1), 1))``
        column names for output DataFrame. Default will look like:
        ['-5', '-4', '-3', '-2', '-1', '0'].

    Returns
    ----------
    out : DataFrame
        Each row is a sequence of `n_sessions` session values where
        the last column matches the value on the date specified by
        the DataFrame index.

    Examples
    --------
    >>> pn.featurize(equity_data, n_sessions, **kwargs)
    """
    #Benchmarking
    #>>> s = 'from __main__ import data\nimport datetime as dt\n'
    #>>> timeit.timeit('data.featurize(data.get("ge", dt.date(1960, 1, 1), 
    #        dt.date(2014, 12, 31)), 256)', setup=s, number=1)
    #1.6771750450134277
    columns = kwargs.get('columns', map(str, range(-n_sessions + 1, 1)))
    selection = kwargs.get('selection', 'Adj Close')
    # empty DataFrame with desired index and column labels
    features = pd.DataFrame(index=equity_data.index[(n_sessions - 1):],
            columns=columns, dtype='float64')
    values = equity_data[selection].values
    for i in range(n_sessions - 1):
        features.iloc[:, i] = values[i:(-n_sessions + i + 1)]
    features.iloc[:, n_sessions - 1] = values[(n_sessions - 1):]
    return features