diff --git a/src/factiva/analytics/integration/files.py b/src/factiva/analytics/integration/files.py
index 8038c43..ad73205 100755
--- a/src/factiva/analytics/integration/files.py
+++ b/src/factiva/analytics/integration/files.py
@@ -7,18 +7,21 @@
 class SnapshotFiles(object):
 
 
-    def read_file(self, filepath, only_stats=False, merge_body=False) -> pd.DataFrame:
+    def read_file(self, filepath, stats_only=False, merge_body=False, all_fields=False) -> pd.DataFrame:
         """Reads a single Dow Jones snapshot datafile
         Parameters
         ----------
         filepath : str
             Relative or absolute file path
-        only_stats : bool, optional
+        stats_only : bool, optional
             Specifies if only file metadata is loaded (True), or if the full article content is loaded (False). On average,
             only_stats loads about 1/10 and is recommended for quick metadata-based analysis. (Default is False)
         merge_body : bool, optional
             Specifies if the body field should be merged with the snippet and this last column being dropped.
             (default is False)
+        all_fields : bool, optional
+            If set, all fields are loaded to the Pandas DataFrame. If set to `True`, parameters `stats_only` and
+            `merge_body` are ignored.
         Returns
         -------
         pandas.DataFrame
@@ -29,19 +32,23 @@ def read_file(self, filepath, only_stats=False, merge_body=False) -> pd.DataFram
             records = [r for r in reader]
             r_df = pd.DataFrame.from_records(records)
 
-        if only_stats is True:
-            r_df = r_df[const.SNAPSHOT_FILE_STATS_FIELDS]
+        if all_fields is False:
+            if stats_only is True:
+                r_df = r_df[const.SNAPSHOT_FILE_STATS_FIELDS]
 
-        if (only_stats is False) & (merge_body is True):
-            r_df['body'] = r_df['snippet'] + '\n\n' + r_df['body']
-            r_df.drop('snippet', axis=1, inplace=True)
+            if (stats_only is False) & (merge_body is True):
+                r_df['body'] = r_df['snippet'] + '\n\n' + r_df['body']
+                r_df.drop('snippet', axis=1, inplace=True)
 
-        if only_stats is False:
-            r_df['body'] = r_df[['body']].apply(lambda x: '{}'.format(x[0]), axis=1)
+            if stats_only is False:
+                r_df['body'] = r_df[['body']].apply(lambda x: '{}'.format(x[0]), axis=1)
 
-        for d_field in const.SNAPSHOT_FILE_DELETE_FIELDS:
-            if d_field in r_df.columns:
-                r_df.drop(d_field, axis=1, inplace=True)
+            for d_field in const.SNAPSHOT_FILE_DELETE_FIELDS:
+                if d_field in r_df.columns:
+                    r_df.drop(d_field, axis=1, inplace=True)
+        else:
+            # TODO: Support merge_body for when all_fields is True
+            r_df['body'] = r_df[['body']].apply(lambda x: '{}'.format(x[0]), axis=1)
 
         r_df['publication_datetime'] = r_df['publication_datetime'].astype('datetime64[ms]')
         r_df['modification_datetime'] = r_df['modification_datetime'].astype('datetime64[ms]')