Update app.py

app.py

@@ -29,19 +29,20 @@ filtered_df = filtered_df[(filtered_df['price'] >= price_range[0]) & (filtered_d
 st.write('Below is the sorted and filtered data:')
 st.write(filtered_df)
 
-# Calculate the average price of each available room in each selected neighbourhood
+# Calculate the average price for each selected neighbourhood
 avg_prices = filtered_df.groupby(['neighbourhood', 'room_type'])['price'].mean().reset_index()
 
-# Plot a histogram of the average prices
-if not avg_prices.empty:
-    st.write('Histogram of Average Price of Each Available Room in Each Neighbourhood:')
+# Pivot the DataFrame to have neighbourhoods as index and room types as columns
+avg_prices_pivot = avg_prices.pivot(index='neighbourhood', columns='room_type', values='price')
+
+# Plot a histogram for each neighbourhood
+if not avg_prices_pivot.empty:
+    st.write('Histogram of Average Price for Each Neighbourhood:')
     fig, ax = plt.subplots()
-    for room_type in avg_prices['room_type'].unique():
-        room_prices = avg_prices[avg_prices['room_type'] == room_type]
-        ax.hist(room_prices['price'], bins=20, alpha=0.5, label=room_type)
-    ax.legend()
-    ax.set_xlabel('Average Price')
-    ax.set_ylabel('Frequency')
+    avg_prices_pivot.plot(kind='bar', ax=ax)
+    ax.set_xlabel('Neighbourhood')
+    ax.set_ylabel('Average Price')
+    plt.xticks(rotation=45, ha='right')
     st.pyplot(fig)
 else:
-    st.write('No data available for selected filters.')
+    st.write('No data available for selected filters.')