Update app.py

app.py

@@ -57,9 +57,16 @@ def color_combined_score(value):
         color = 'none'
     return f'background-color: {color};'
 
+def filter_incomplete_stocks(df):
+    # Assuming that 'N/A' represents missing data, replace it with NaN
+    df.replace('N/A', pd.NA, inplace=True)
+    # Drop rows with any NaN values in the specified columns
+    return df.dropna(subset=['P/E Ratio', 'P/B Ratio', 'P/S Ratio', 'Debt to Equity Ratio', 'Return on Equity', 'Book-to-Market Ratio'])
+    
 # User interface in Streamlit
 st.title('S&P 500 Stock Comparison Tool')
 
+
 # Load the current S&P 500 list and averages
 sp500_list = get_sp500_list()
 sp500_averages = load_sp500_averages(sp500_averages_path)
@@ -68,21 +75,22 @@ sp500_averages = load_sp500_averages(sp500_averages_path)
 scores_df = calculate_combined_scores_for_stocks(sp500_list, sp500_averages)
 scores_df_sorted = scores_df.sort_values(by='Combined Score', ascending=False)
 
+# Filter out stocks with incomplete data
+scores_df_filtered = filter_incomplete_stocks(scores_df_sorted)
+
 # Layout for displaying overview and details
 col1, col2 = st.columns([3, 5])  # For example, this will give the first column 3/8 of the width
 
 with col1:
     st.subheader("Stock Overview")
-    # Make sure to convert 'Combined Score' to numeric if it's not already
-    scores_df_sorted['Combined Score'] = pd.to_numeric(scores_df_sorted['Combined Score'], errors='coerce')
     # Apply color based on 'Combined Score' value and display the DataFrame
-    styled_scores_df = scores_df_sorted.style.applymap(color_combined_score, subset=['Combined Score'])
-    st.dataframe(styled_scores_df, height=600)
+    styled_scores_df = scores_df_filtered.style.applymap(color_combined_score, subset=['Combined Score'])
+    st.dataframe(styled_scores_df)
 
 with col2:
     st.subheader("Stock Details")
-    # Get the sorted list of ticker symbols based on the combined score
-    sorted_tickers = scores_df_sorted['Stock'].tolist()
+    # Use the filtered DataFrame to create the dropdown for stock selection
+    sorted_tickers = scores_df_filtered['Stock'].tolist()
     ticker_symbol = st.selectbox('Select a stock for details', options=sorted_tickers)
     if ticker_symbol:
         with st.spinner(f'Fetching data for {ticker_symbol}...'):