Update app.py

app.py

@@ -23,30 +23,45 @@ data = load_data()
 data['Year Acquired'] = pd.to_numeric(data['Year Acquired'], errors='coerce')
 data['Square Footage'] = pd.to_numeric(data['Square Footage'], errors='coerce')
 
-# Visualization 1: Distribution of Buildings by Year Acquired
-st.header("Distribution of Buildings by Year Acquired")
-year_acquired_chart = alt.Chart(data).mark_bar(color='steelblue').encode(
-    alt.X('Year Acquired:Q', bin=alt.Bin(maxbins=20), title="Year Acquired"),
-    alt.Y('count():Q', title="Number of Buildings")
+# Data Preprocessing for New Visualization
+# Convert 'Year Acquired' and 'Square Footage' to numeric, handling errors
+data['Year Acquired'] = pd.to_numeric(data['Year Acquired'], errors='coerce')
+data['Square Footage'] = pd.to_numeric(data['Square Footage'], errors='coerce')
+
+# Remove rows with missing or invalid data in 'Year Acquired' or 'Square Footage'
+data_clean = data.dropna(subset=['Year Acquired', 'Square Footage'])
+
+# Group by 'Year Acquired' and sum 'Square Footage'
+yearly_square_footage = data_clean.groupby('Year Acquired')['Square Footage'].sum().reset_index()
+
+# Visualization: Total Square Footage Acquired Over Time
+st.header("Total Square Footage Acquired Over Time")
+line_chart = alt.Chart(yearly_square_footage).mark_line(point=True, color='seagreen').encode(
+    alt.X('Year Acquired:Q', title="Year Acquired"),
+    alt.Y('Square Footage:Q', title="Total Square Footage"),
+    tooltip=['Year Acquired', 'Square Footage']
 ).properties(
     width=700,
     height=400,
-    title="Distribution of Buildings by Year Acquired"
-)
-st.altair_chart(year_acquired_chart, use_container_width=True)
+    title="Total Square Footage Acquired Over Time"
+).interactive()
+
+st.altair_chart(line_chart, use_container_width=True)
 
 st.markdown("""
 ### Explanation
-This visualization highlights the distribution of buildings based on the year they were acquired. 
+This line chart illustrates the total square footage of buildings acquired each year, highlighting trends in acquisition sizes over time.
 - **Design Choices**:
-  - A bar chart was chosen for clarity, with years grouped into bins for better aggregation.
-  - The steel blue color provides a neutral tone and avoids distraction.
-  - Axis labels and titles were carefully chosen for readability.
+  - A line chart was selected to effectively display changes over time.
+  - Data points are marked to emphasize individual years.
+  - The seagreen color provides a calm and clear visual.
+  - Interactive tooltips offer precise values upon hovering.
 - **Potential Improvements**:
-  - If more time was available, I would add an interactive filter to explore specific years.
-  - It would also be useful to compare acquisition trends by agency or location.
+  - Incorporating filters to view trends for specific agencies or locations.
+  - Adding a moving average line to smooth out year-to-year fluctuations.
 """)
 
+
 # Visualization 2: Square Footage by Usage Description
 st.header("Square Footage by Usage Description")
 usage_chart = alt.Chart(data).mark_boxplot().encode(