Basic output of Pandas DataFrames

espnff/league.py

@@ -1,5 +1,8 @@
 import requests
 
+from pandas.io.json import json_normalize
+import pandas as pd
+
 from .utils import (two_step_dominance,
                     power_points, )
 from .team import Team
@@ -131,3 +134,59 @@ def scoreboard(self, week=None):
                     matchup.away_team = team
 
         return result
+
+    def team_df(self):
+        """ Returns a Pandas DataFrame with team data """
+        variables = self.teams[0].__dict__.keys()
+        df = pd.DataFrame([[getattr(i, j) for j in variables] for i in self.teams], columns=variables)
+        return df
+
+    def player_df(self):
+        """ Returns a Pandas DataFrame with raw player data """
+        params = {
+            'leagueId': self.league_id,
+            'offset': None,
+            'limit': '40',
+            'availabilityFilter': '-1',
+            'slotCategoryFilter': '-1',
+            'useCurrentSeasonRealStats': 'true',
+            'useCurrentSeasonProjectedStats': 'true',
+            'useCurrentPeriodRealStats': 'true',
+            'useCurrentPeriodProjectedStats': 'true',
+            'usePreviousPeriodRealStats': 'true',
+            'usePreviousSeasonRealStats': 'true',
+            'processAverages': 'true',
+            'includeRankings': 'true',
+            'includeLatestNews': 'true',
+            'includeProjectionText': 'true',
+        }
+
+        cookies = None
+        if self.espn_s2 and self.swid:
+            cookies = {
+                'espn_s2': self.espn_s2,
+                'SWID': self.swid
+            }
+
+        player_data = []
+
+        for i in range(20):
+            params['offset'] = i
+
+            r = requests.get('%splayerInfo' % (self.ENDPOINT,), params=params, cookies=cookies)
+            self.status = r.status_code
+            data = r.json()
+
+            if self.status == 401:
+                raise PrivateLeagueException(data['error'][0]['message'])
+            elif self.status == 404:
+                raise InvalidLeagueException(data['error'][0]['message'])
+            elif self.status != 200:
+                raise UnknownLeagueException('Unknown %s Error' % self.status)
+
+            player_data.extend(data['playerInfo']['players'])
+
+        df = json_normalize(player_data)
+        df = df.select(lambda x: 'rawStats' not in x and 'appliedStats' not in x, axis=1)  # remove excess statistics
+
+        return df