diff --git a/ngrams/term_frequencies.py b/ngrams/term_frequencies.py
index 30e1336..9703f3c 100755
--- a/ngrams/term_frequencies.py
+++ b/ngrams/term_frequencies.py
@@ -119,9 +119,10 @@ def weekly_tf(partition,
 
     if reddit_dataset == 'comments':
         tf_func = tf_comments
+        nullable_schema = False
     elif reddit_dataset == 'posts':
         tf_func = tf_posts
-
+        nullable_schema = True
     dataset = ds.dataset(f"{input_parquet}/{partition}", format='parquet')
     if not os.path.exists(output_10p_sample_path):
         os.mkdir(output_10p_sample_path)
@@ -140,16 +141,16 @@ def weekly_tf(partition,
     if reddit_dataset == 'posts':
         batches = dataset.to_batches(columns=['CreatedAt','subreddit','title','author'])
 
-    schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
-                        pa.field('term', pa.string(), nullable=False),
-                        pa.field('week', pa.date32(), nullable=False),
-                        pa.field('tf', pa.int64(), nullable=False)]
+    schema = pa.schema([pa.field('subreddit', pa.string(), nullable=nullable_schema),
+                        pa.field('term', pa.string(), nullable=nullable_schema),
+                        pa.field('week', pa.date32(), nullable=nullable_schema),
+                        pa.field('tf', pa.int64(), nullable=nullable_schema)]
     )
 
-    author_schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
-                               pa.field('author', pa.string(), nullable=False),
-                               pa.field('week', pa.date32(), nullable=False),
-                               pa.field('tf', pa.int64(), nullable=False)]
+    author_schema = pa.schema([pa.field('subreddit', pa.string(), nullable=nullable_schema),
+                               pa.field('author', pa.string(), nullable=nullable_schema),
+                               pa.field('week', pa.date32(), nullable=nullable_schema),
+                               pa.field('tf', pa.int64(), nullable=nullable_schema)]
     )
 
     dfs = (b.to_pandas() for b in batches)