diff --git a/slots/slots.py b/slots/slots.py
index d5786d4..9d9930d 100755
--- a/slots/slots.py
+++ b/slots/slots.py
@@ -39,9 +39,9 @@ def __init__(self, num_bandits=3, probs=None, payouts=None, live=True,
         stop_criterion : dict
             Stopping criterion (str) and threshold value (float).
         '''
-
+        self.num_bandits = num_bandits
         self.choices = []
-
+        self.payout_values = []
         if not probs:
             if not payouts:
                 if live:
@@ -135,6 +135,7 @@ def _run(self, strategy, parameters=None):
         choice = self.run_strategy(strategy, parameters)
         self.choices.append(choice)
         payout = self.bandits.pull(choice)
+        self.payout_values.append(payout)
         if payout is None:
             print('Trials exhausted. No more values for bandit', choice)
             return None
@@ -327,20 +328,47 @@ def best(self):
         else:
             return np.argmax(self.wins/(self.pulls+0.1))
 
-    def est_payouts(self):
+    def current(self):
         '''
-        Calculate current estimate of average payout for each bandit.
+        Return last choice of bandit.
 
         Returns
         -------
-        array of floats or None
+        int
+            Index of bandit
         '''
 
         if len(self.choices) < 1:
             print('slots: No trials run so far.')
             return None
         else:
-            return self.wins/(self.pulls+0.1)
+            return self.choices[-1]
+
+    def est_payouts(self, bandit=None):
+        '''
+        Calculate current estimate of average payout for each bandit.
+        
+        Parameters
+        ----------
+        bandit : None
+            If a bandit is selected, return the payout for that bandit, otherwise return all payouts. 
+
+        Returns
+        -------
+        array of floats or None
+        '''
+        if not bandit:
+            if len(self.choices) < 1:
+                print('slots: No trials run so far.')
+                return None
+            else:
+                return self.wins/(self.pulls+0.1)
+        else:
+            if len(self.choices) < 1:
+                print('slots: No trials run so far.')
+                return None
+            else:
+                return (self.wins/(self.pulls+0.1))[bandit]
 
     def regret(self):
         '''
@@ -430,6 +458,67 @@ def online_trial(self, bandit=None, payout=None, strategy='eps_greedy',
             return {'new_trial': True,
                     'choice': self.run_strategy(strategy, parameters),
                     'best': self.best()}
+    
+
+    def multiple_trials(self, bandits=None, payouts=None, method = 'hard', strategy='eps_greedy',
+                     parameters=None):
+        '''
+        Feeds two arrays in and based on those results returns the next trial. 
+        This really isn't optimized, there's a much better way of doing this if we don't 
+        care about maintaining the workflow.
+
+        Parameters
+        ----------
+        bandit : array of ints
+            Bandit index
+        payout : array of floats
+            Payout value
+        method : string
+            Name of summing strategy 
+            If 'hard' then it manually iterates over each row 
+            If 'lazy' it attempts to sum it as an array and only add final product
+        strategy : string
+            Name of update strategy
+        parameters : dict
+            Parameters for update strategy function
+
+        Returns
+        -------
+        dict
+            Format: {'new_trial': boolean, 'choice': int, 'best': int}
+        '''
+        bandits = bandits.values
+        payouts = payouts.values
+        if len(payouts) != len(bandits):
+            raise Exception('slots.online_trials: number of bandits is different from number of payouts')
+        else:
+            if method == 'hard':
+                    for x in range(0,len(payouts)):
+                        if bandits[x] is not None and payouts[x] is not None:
+                            self.update(bandit=bandits[x], payout=payouts[x])
+                        else:
+                            raise Exception('slots.online_trial: bandit and/or payout value'
+                                    ' missing.')
+
+            else if method = 'lazy':
+                banditos = np.array(bandits)
+                self.choices.extend(list(bandits))
+                self.payout_values.extend(list(payouts))
+                for y in list(set(bandits)):
+                    indices = np.where(banditos == y)[0]
+                    payola = [payouts[i] for i in indices]
+                    self.pulls[y] += len(payola)
+                    self.wins[y] += sum(payola)
+                    self.bandits.payouts[y] += sum(payola)
+
+        if self.crit_met():
+            return {'new_trial': False, 'choice': self.best(),
+                    'best': self.best()}
+        else:
+            return {'new_trial': True,
+                    'choice': self.run_strategy(strategy, parameters),
+                    'best': self.best()}
+
 
     def update(self, bandit, payout):
         '''
@@ -445,12 +534,17 @@ def update(self, bandit, payout):
         -------
         None
         '''
-
+        self.payout_values.append(payout)
         self.choices.append(bandit)
         self.pulls[bandit] += 1
         self.wins[bandit] += payout
         self.bandits.payouts[bandit] += payout
 
+    def info(self):
+        '''
+        Default: display number of bandits, wins, and estimated probabilities
+        '''
+        return('number of bandits:',self.num_bandits, 'number of wins:', self.wins, 'estimated payouts:', self.est_payouts())
 
 class Bandits():
     '''
@@ -511,5 +605,3 @@ def pull(self, i):
             else:
                 return 0.0
 
-    def info(self):
-        pass