fix temporal charts: filter out empty time periods to prevent sparse data gaps

src/streamlit_app.py

@@ -550,13 +550,16 @@ def create_temporal_visualizations(df: pd.DataFrame) -> Dict[str, go.Figure]:
     )
     figures['heatmap'] = fig_heatmap
     
-    # 2. Time series with rolling average
+    # 2. Time series with rolling average - only include days with actual data
     daily_stats = df.set_index('created_utc').resample('D').agg({
         'id': 'count',
         'score': 'mean',
         'num_comments': 'mean'
     }).rename(columns={'id': 'post_count'})
-    
+
+    # Filter out days with no posts to prevent misleading gaps
+    daily_stats = daily_stats[daily_stats['post_count'] > 0]
+
     # Calculate rolling averages
     daily_stats['post_count_ma7'] = daily_stats['post_count'].rolling(window=7, min_periods=1).mean()
     daily_stats['score_ma7'] = daily_stats['score'].rolling(window=7, min_periods=1).mean()
@@ -623,9 +626,12 @@ def create_temporal_visualizations(df: pd.DataFrame) -> Dict[str, go.Figure]:
             'score': ['mean', 'sum'],
             'num_comments': ['mean', 'sum']
         })
-        
+
+        # Filter out months with no posts
+        monthly_data = monthly_data[monthly_data[('id', 'count')] > 0]
+
         fig_monthly = go.Figure()
-        
+
         fig_monthly.add_trace(go.Bar(
             x=monthly_data.index,
             y=monthly_data[('id', 'count')],
@@ -659,9 +665,12 @@ def create_temporal_visualizations(df: pd.DataFrame) -> Dict[str, go.Figure]:
             'score': ['mean', 'sum'],
             'num_comments': ['mean', 'sum']
         })
-        
+
+        # Filter out weeks with no posts
+        weekly_data = weekly_data[weekly_data[('id', 'count')] > 0]
+
         fig_weekly = go.Figure()
-        
+
         fig_weekly.add_trace(go.Bar(
             x=weekly_data.index,
             y=weekly_data[('id', 'count')],