sportsref/nfl/boxscores.py
def starters(self):
        """Returns a DataFrame where each row is an entry in the starters table
        from PFR.

        The columns are:
        * player_id - the PFR player ID for the player (note that this column
        is not necessarily all unique; that is, one player can be a starter in
        multiple positions, in theory).
        * playerName - the listed name of the player; this too is not
        necessarily unique.
        * position - the position at which the player started for their team.
        * team - the team for which the player started.
        * home - True if the player's team was at home, False if they were away
        * offense - True if the player is starting on an offensive position,
        False if defense.

        :returns: A pandas DataFrame. See the description for details.
        """
        doc = self.get_doc()
        a = doc('table#vis_starters')
        h = doc('table#home_starters')
        data = []
        for h, table in enumerate((a, h)):
            team = self.home() if h else self.away()
            for i, row in enumerate(table('tbody tr').items()):
                datum = {}
                datum['player_id'] = sportsref.utils.rel_url_to_id(
                    row('a')[0].attrib['href']
                )
                datum['playerName'] = row('th').text()
                datum['position'] = row('td').text()
                datum['team'] = team
                datum['home'] = (h == 1)
                datum['offense'] = (i <= 10)
                data.append(datum)
        return pd.DataFrame(data)
def starters(self):
        doc = self.get_doc()
        a = doc('table#vis_starters')
        h = doc('table#home_starters')
        data = []
        for h, table in enumerate((a, h)):
            team = self.home() if h else self.away()
            for i, row in enumerate(table('tbody tr').items()):
                datum = {}
                datum['player_id'] = sportsref.utils.rel_url_to_id(
                    row('a')[0].attrib['href']
                )
                datum['playerName'] = row('th').text()
                datum['position'] = row('td').text()
                datum['team'] = team
                datum['home'] = (h == 1)
                datum['offense'] = (i <= 10)
                data.append(datum)
        return pd.DataFrame(data)
def starters ( self ) : doc = self . get_doc ( ) a = doc ( 'table#vis_starters' ) h = doc ( 'table#home_starters' ) data = [ ] for h , table in enumerate ( ( a , h ) ) : team = self . home ( ) if h else self . away ( ) for i , row in enumerate ( table ( 'tbody tr' ) . items ( ) ) : datum = { } datum [ 'player_id' ] = sportsref . utils . rel_url_to_id ( row ( 'a' ) [ 0 ] . attrib [ 'href' ] ) datum [ 'playerName' ] = row ( 'th' ) . text ( ) datum [ 'position' ] = row ( 'td' ) . text ( ) datum [ 'team' ] = team datum [ 'home' ] = ( h == 1 ) datum [ 'offense' ] = ( i <= 10 ) data . append ( datum ) return pd . DataFrame ( data )
Returns a DataFrame where each row is an entry in the starters table
        from PFR.

        The columns are:
        * player_id - the PFR player ID for the player (note that this column
        is not necessarily all unique; that is, one player can be a starter in
        multiple positions, in theory).
        * playerName - the listed name of the player; this too is not
        necessarily unique.
        * position - the position at which the player started for their team.
        * team - the team for which the player started.
        * home - True if the player's team was at home, False if they were away
        * offense - True if the player is starting on an offensive position,
        False if defense.

        :returns: A pandas DataFrame. See the description for details.
Returns a DataFrame where each row is an entry in the starters table from PFR .