def featurize(equity_data, n_sessions, **kwargs):
<EOL>columns = kwargs.get('<STR_LIT>', map(str, range(-n_sessions + <NUM_LIT:1>, <NUM_LIT:1>)))<EOL>selection = kwargs.get('<STR_LIT>', '<STR_LIT>')<EOL>features = pd.DataFrame(index=equity_data.index[(n_sessions - <NUM_LIT:1>):],<EOL>columns=columns, dtype='<STR_LIT>')<EOL>values = equity_data[selection].values<EOL>for i in range(n_sessions - <NUM_LIT:1>):<EOL><INDENT>features.iloc[:, i] = values[i:(-n_sessions + i + <NUM_LIT:1>)]<EOL><DEDENT>features.iloc[:, n_sessions - <NUM_LIT:1>] = values[(n_sessions - <NUM_LIT:1>):]<EOL>return features<EOL>
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)