diff --git a/ebay_api.py b/ebay_api.py
index ab3e87d..7fdb368 100644
--- a/ebay_api.py
+++ b/ebay_api.py
@@ -41,7 +41,7 @@ class FindingApi:
             "SERVICE-VERSION":"1.13.0",
             "RESPONSE-DATA-FORMAT":"JSON",
             "categoryId":category_id,
-            "paginationInput.entriesPerPage":"10", # TODO change back to max = 100
+            "paginationInput.entriesPerPage":"100",
             "paginationInput.PageNumber":i,
             "itemFilter(0).name":"Condition",
             "itemFilter(0).value":"Used"
@@ -248,7 +248,7 @@ class CurateData:
             raw_data = json.load(f)
             return raw_data
 
-    def raw_df(self, raw_data):
+    def raw_df(self, raw_data): # TODO not dropping dupes, and is appending raw_data for some reason
         '''
         creates pandas df from raw json and saves master raw csv file as raw_df.csv.
         Indended to be used inline with direct
@@ -256,14 +256,16 @@ class CurateData:
         '''
         to_json = json.dumps(raw_data)
         raw_df = pd.read_json(to_json)
-        raw_df.to_csv('raw_df.csv', mode='a')
-        raw_df = pd.read_csv('raw_df.csv', index_col=0)
-        raw_df.drop_duplicates(subset=['ItemID']).reset_index(drop=True) # drops dupes after appending new data. (ShoppingApi call might include dupes)
-        raw_df.to_csv('raw_df.csv', mode='a') # TODO this might still only save the unmodified/undropped original. check to make sure 
+        raw_df.to_csv('raw_df.csv') # NOTE not append mode because raw_df is made from the master raw_data.txt file
+        #raw_df = pd.read_csv('raw_df.csv', index_col=0)
+        #raw_df.drop_duplicates(subset=['ItemID']).reset_index(drop=True) # may not need this
+        #raw_df.to_csv('raw_df.csv')
+
+        # TODO still saving "Unnamed:0" column
 
         return raw_df
 
-    def to_training(self, raw_data): # NOTE need to create copies not views 
+    def to_training(self, raw_data):
         '''
         creates first pass of potential labels for training set. This is the base
         df used to produce other training sets to use.
@@ -271,14 +273,14 @@ class CurateData:
         raw_df = self.raw_df(raw_data)
         interm_df1 = raw_df.loc[:,['ItemID', 'PictureURL', 'PrimaryCategoryID', 'PrimaryCategoryName', 'Title', 'ItemSpecifics']]
         interm_df1[['ItemID', 'PrimaryCAegoryID']] = interm_df1.loc[:, ['ItemID', 'PrimaryCategoryID']].astype(str)
-        training = interm_df1
-        return training # TODO RENAME THIS FUNC AND RETURN VALUE
+        training = interm_df1.dropna(subset=['ItemSpecifics'])
+        return training # TODO RENAME THIS FUNC AND its RETURN VALUE
 
     def class_training(self, training):
         '''Training set for multiclass portion of training set. Used to train
         seprately from multilabel portion
         '''
-        class_training = training.loc[:, ['PictureURL', 'PrimaryCategoryID']]
+        class_training = training.loc[:, ['PictureURL', 'PrimaryCategoryID']] 
         return class_training
 
     def nvl_training(self, training):
@@ -326,7 +328,7 @@ class CurateData:
                 'PictureURL', 'Style', 'Department', 'Type', 'Gender', 'Closure', 'Performance/Activity',
                 'Accents', 'Occasion', 'Toe Shape', 'Pattern', 'Activity',
                 'Heel Style', 'Fastening', 'Heel Type', 'Toe Type', 'Departement',
-                'Product Type', 'Sub Style', 'Season', 'Theme', 'Upper Material',
+                'Product Type', 'Sub Style', 'Season', 'Theme', 'Upper Material'
                 ]
         # May be no difference between Product type and sub style; fastening and
         # closure; toe shape and toe type; occasion and performance/activity;
@@ -336,8 +338,9 @@ class CurateData:
         # user created item specifics, leaving only predefined ebay item specs
 
         user_input = input('drop or keep cols?:')
+
         if 'keep' in user_input:
-            dropd = nvl_training.loc[:,col_keep]
+            dropd = nvl_training.reindex([col_keep])
         else:
             dropd = nvl_training.drop(col_drop, axis=1)
         return dropd
@@ -364,7 +367,7 @@ class CurateData:
         expanded_dropd = self.extract_df(expanded_dropd) # convert lists to values
 
         temp_pics_source_list = list(set(expanded_class.PictureURL.to_list())) # prolly need to create set long before df... immediately after Shopping or trading call
-        # defined in the download function
+        #: defined in the download function
 
 
         try:
@@ -380,15 +383,15 @@ class CurateData:
 
         # Append to master training dataframes, drop potential dupes and save
 
-        expanded_class.to_csv('expanded_class.csv', mode='a', encoding='utf-8')
-        expanded_class = pd.read_csv('expanded_class.csv', index_col=0)
-        expanded_class.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
-        expanded_class.to_csv('expanded_class.csv', mode='a', encoding='utf-8') # TODO see line 235 about views and copies
+        expanded_class.to_csv('expanded_class.csv')
+        # expanded_class = pd.read_csv('expanded_class.csv', index_col=0)
+        # expanded_class.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
+        # expanded_class.to_csv('expanded_class.csv', mode='a', encoding='utf-8') # TODO see line 235 about views and copies
 
-        expanded_dropd.to_csv('expanded_dropd.csv', mode='a', encoding='utf-8')
-        expanded_dropd = pd.read_csv('expanded_dropd.csv', index_col=0)
-        expanded_dropd.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
-        expanded_dropd.to_csv('expanded_dropd.csv', mode='a', encoding='utf-8')
+        expanded_dropd.to_csv('expanded_dropd.csv')
+        # expanded_dropd = pd.read_csv('expanded_dropd.csv', index_col=0)
+        # expanded_dropd.drop_duplicates(subset=['PictureURL']).reset_index(drop=True)
+        # expanded_dropd.to_csv('expanded_dropd.csv', mode='a', encoding='utf-8')
 
         return expanded_class, expanded_dropd