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Position Pool

pangadfs.pospool

PospoolDefault()

Bases: PospoolBase

Source code in pangadfs/base.py 
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def __init__(self):
    logging.getLogger(__name__).addHandler(logging.NullHandler())

pospool(*, pool, posfilter, column_mapping, flex_positions=('RB', 'WR', 'TE'), **kwargs)

Creates initial position pool

Parameters:

Name Type Description Default
pool DataFrame
required
posfilter Dict[str, int]

filter out low scorers by position

 required
column_mapping Dict[str, str]

column names for player, position, salary, projection

 required
flex_positions Iterable[str]

e.g. (WR, RB, TE)

 ('RB', 'WR', 'TE')
**kwargs

Keyword arguments for plugins (other than default)

 {}

Returns:

Type Description
Dict[str, DataFrame]

Dict[str, pd.DataFrame] where keys == posfilter.keys
Source code in pangadfs/pospool.py 
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def pospool(self,
            *,
            pool: pd.DataFrame,
            posfilter: Dict[str, int],
            column_mapping: Dict[str, str],
            flex_positions: Iterable[str] = ('RB', 'WR', 'TE'),
            **kwargs
            ) -> Dict[str, pd.DataFrame]:
    """Creates initial position pool

    Args:
        pool (pd.DataFrame):
        posfilter (Dict[str, int]): filter out low scorers by position
        column_mapping (Dict[str, str]): column names for player, position, salary, projection
        flex_positions (Iterable[str]): e.g. (WR, RB, TE)
        **kwargs: Keyword arguments for plugins (other than default)

    Returns:
        Dict[str, pd.DataFrame] where keys == posfilter.keys

    """
    d = {}
    poscol = column_mapping.get('position', 'pos')
    pointscol = column_mapping.get('points', 'proj')
    salcol = column_mapping.get('salary', 'salary')
    for position, thresh in posfilter.items():
        if position == 'FLEX':
            tmp = pool.loc[(pool[poscol].isin(flex_positions)) & (pool[pointscol] >= thresh), [pointscol, salcol]]      
        else:           
            tmp = pool.loc[(pool[poscol] == position) & (pool[pointscol] >= thresh), [pointscol, salcol]]
        prob_ = (tmp[pointscol] / tmp[salcol]) * 1000
        prob_ = prob_ / prob_.sum()
        d[position] = tmp.assign(prob=prob_)
    return d