Update app.py

app.py

@@ -54,11 +54,20 @@ This bar chart illustrates the number of buildings in each county, highlighting
 """)
 
 # Visualization 2: Year-wise Construction of Buildings
-st.header("2. Year-wise Construction of Buildings")
-yearly_construction = data['Year Constructed'].dropna().astype(int).value_counts().reset_index()
+# Data Preprocessing for Year-wise Construction Visualization
+# Convert 'Year Constructed' to numeric, handling errors
+data['Year Constructed'] = pd.to_numeric(data['Year Constructed'], errors='coerce')
+
+# Filter out rows where 'Year Constructed' is 0 or NaN
+data_filtered = data[(data['Year Constructed'] > 0) & (~data['Year Constructed'].isna())]
+
+# Group by 'Year Constructed' and count the number of buildings
+yearly_construction = data_filtered['Year Constructed'].value_counts().reset_index()
 yearly_construction.columns = ['Year Constructed', 'Building Count']
 yearly_construction = yearly_construction.sort_values('Year Constructed')
 
+# Visualization: Year-wise Construction of Buildings
+st.header("2. Year-wise Construction of Buildings")
 year_chart = alt.Chart(yearly_construction).mark_line(point=True, color='orange').encode(
     alt.X('Year Constructed:Q', title='Year Constructed'),
     alt.Y('Building Count:Q', title='Number of Buildings'),
@@ -84,6 +93,7 @@ This line chart displays the number of buildings constructed each year, revealin
   - Allow users to filter the data by building type or agency to explore specific trends.
 """)
 
+
 # Footer
 st.markdown("""
 ---