Update app.py

app.py

@@ -3,17 +3,14 @@ import streamlit as st
 import matplotlib.pyplot as plt
 
 # Read the CSV file into a Pandas DataFrame
-df = pd.read_csv('cph_airbnb_listings.csv')
+df = pd.read_csv('/Users/mads/Desktop/Cand. Merc IM/2. semester/2.4 Digital Innovation/1. Introduction/S1_files/cph_airbnb_listings.csv')
 
 # Create a Streamlit app
-st.title('AirBnB bookings in Copenhagen')
+st.title('Sorted and Filtered Data by Neighbourhood and Price')
 
 # Allow user to add filters for neighbourhoods
 selected_neighbourhoods = st.sidebar.multiselect('Select Neighbourhood(s)', df['neighbourhood'].unique())
 
-# Allow user to add filter for room type
-selected_room_type = st.sidebar.selectbox('Select Room Type', ['Private room', 'Entire home/apt'])
-
 # Allow user to set a price range filter
 price_range = st.sidebar.slider('Select Price Range', min_value=0, max_value=1000, step=10, value=(0, 1000))
 
@@ -21,15 +18,13 @@ price_range = st.sidebar.slider('Select Price Range', min_value=0, max_value=100
 filtered_df = df.copy()
 if selected_neighbourhoods:
     filtered_df = filtered_df[filtered_df['neighbourhood'].isin(selected_neighbourhoods)]
-if selected_room_type:
-    filtered_df = filtered_df[filtered_df['room_type'] == selected_room_type]
 filtered_df = filtered_df[(filtered_df['price'] >= price_range[0]) & (filtered_df['price'] <= price_range[1])]
 
 # Display the filtered DataFrame
 st.write('Below is the sorted and filtered data:')
 st.write(filtered_df)
 
-# Calculate the average price for each selected neighbourhood
+# Calculate the average price for each selected neighbourhood and room type
 avg_prices = filtered_df.groupby(['neighbourhood', 'room_type'])['price'].mean().reset_index()
 
 # Pivot the DataFrame to have neighbourhoods as index and room types as columns