Update app.py

app.py

@@ -2,64 +2,124 @@ import streamlit as st
 import pandas as pd
 import altair as alt
 
-# Load hospital dataset
-hospitals_df = pd.read_csv("https://data.medicare.gov/api/views/xubh-q36u/rows.csv?accessType=DOWNLOAD")
+# Define list of largest hospitals with latitude and longitude
+largest_hospitals = [
+    {
+        'name': 'Florida Hospital Orlando',
+        'city': 'Orlando',
+        'state': 'FL',
+        'zip_code': '32803',
+        'bed_count': 2411,
+        'lat': 28.562229,
+        'lng': -81.362976
+    },
+    {
+        'name': 'Cleveland Clinic',
+        'city': 'Cleveland',
+        'state': 'OH',
+        'zip_code': '44195',
+        'bed_count': 1730,
+        'lat': 41.501669,
+        'lng': -81.621275
+    },
+    {
+        'name': 'Mayo Clinic',
+        'city': 'Rochester',
+        'state': 'MN',
+        'zip_code': '55905',
+        'bed_count': 1372,
+        'lat': 44.020634,
+        'lng': -92.463476
+    },
+    {
+        'name': 'NewYork-Presbyterian Hospital-Columbia and Cornell',
+        'city': 'New York',
+        'state': 'NY',
+        'zip_code': '10032',
+        'bed_count': 2332,
+        'lat': 40.840886,
+        'lng': -73.942184
+    },
+    {
+        'name': 'UCHealth University of Colorado Hospital',
+        'city': 'Aurora',
+        'state': 'CO',
+        'zip_code': '80045',
+        'bed_count': 672,
+        'lat': 39.742401,
+        'lng': -104.834694
+    },
+    {
+        'name': 'Houston Methodist Hospital',
+        'city': 'Houston',
+        'state': 'TX',
+        'zip_code': '77030',
+        'bed_count': 1063,
+        'lat': 29.710292,
+        'lng': -95.399262
+    },
+    {
+        'name': 'Johns Hopkins Hospital',
+        'city': 'Baltimore',
+        'state': 'MD',
+        'zip_code': '21287',
+        'bed_count': 1293,
+        'lat': 39.297082,
+        'lng': -76.590726
+    },
+    {
+        'name': 'Massachusetts General Hospital',
+        'city': 'Boston',
+        'state': 'MA',
+        'zip_code': '02114',
+        'bed_count': 1032,
+        'lat': 42.363371,
+        'lng': -71.068635
+    },
+    {
+        'name': 'University of Michigan Hospitals-Michigan Medicine',
+        'city': 'Ann Arbor',
+        'state': 'MI',
+        'zip_code': '48109',
+        'bed_count': 1145,
+        'lat': 42.282531,
+        'lng': -83.728376
+    },
+    {
+        'name': 'Mount Sinai Hospital',
+        'city': 'New York',
+        'state': 'NY',
+        'zip_code': '10029',
+        'bed_count': 1168,
+        'lat': 40.789866,
+        'lng': -73.952348
+    }
+]
 
-# Filter for the largest hospital in each state
-largest_hospitals_df = hospitals_df.loc[hospitals_df.groupby("State")["Hospital overall rating"].idxmax()]
-
-# Select columns to display
-cols_to_display = ["State", "Hospital Name", "City", "Zip Code", "lat", "lng"]
-
-# Create a Streamlit table to display the largest hospitals
-st.table(largest_hospitals_df[cols_to_display])
+# Convert list of largest hospitals to a Pandas DataFrame
+largest_hospitals_df = pd.DataFrame(largest_hospitals)
 
 # Define chart functions
-def stacked_bar_chart():
-    chart = alt.Chart(largest_hospitals_df).mark_bar().encode(
-        x=alt.X('State:N'),
-        y=alt.Y('count():Q', stack="normalize"),
-        color=alt.Color('Hospital Type:N'),
-        tooltip=['Hospital Name', 'City', 'Hospital overall rating']
-    ).properties(
-        width=700,
-        height=400,
-        title='Number of Hospitals by State and Type'
-    )
-    text = chart.mark_text(
-        align='center',
-        baseline='middle',
-        dx=0,
-        dy=5,
-        color='white'
-    ).encode(
-        text=alt.Text('count():Q', format='.1f')
-    )
-    st.altair_chart(chart + text)
-
-def bump_chart():
-    chart = alt.Chart(largest_hospitals_df).transform_rank(
-        'Hospital overall rating',
-        groupby=['State']
-    ).mark_line().encode(
-        x='rank:Q',
-        y=alt.Y('Hospital Name:N', sort='-x'),
-        color=alt.Color('State:N'),
-        tooltip=['City', 'Zip Code', 'Hospital overall rating']
+def map_chart():
+    chart = alt.Chart(largest_hospitals_df).mark_circle().encode(
+        longitude='lng:Q',
+        latitude='lat:Q',
+        size=alt.Size('bed_count:Q', title='Bed Count'),
+        color=alt.Color('state:N', legend=alt.Legend(title='State')),
+        tooltip=['name', 'bed_count', 'city', 'state']
     ).properties(
         width=700,
-        height=400,
-        title='Hospital Rankings by State'
+        height=500,
+        title='Location of Largest Hospitals in the US'
     )
     st.altair_chart(chart)
 
 # Define chart buttons
 st.sidebar.header('Select a Chart')
-chart_options = ['Stacked Bar Chart with Text Overlay', 'Bump Chart']
+chart_options = ['Map Chart']
 chart_choice = st.sidebar.selectbox('', chart_options)
 
 # Call chart functions based on user input
-if chart_choice == 'Stacked Bar Chart with Text Overlay':
-    stacked_bar_chart()
-elif chart_choice == 'Bump Chart':
-    bump_chart()
+if chart_choice == 'Map Chart':
+    map_chart()
+