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app.py

@@ -1,73 +1,79 @@
-# INSTRUCTIONS:
-# 1. Open a "Terminal" by: View --> Terminal OR just the "Terminal" through the hamburger menu
-# 2. run in terminal with: streamlit run app.py
-# 3. click the "Open in Browser" link that pops up OR click on "Ports" and copy the URL
-# 4. Open a Simple Browswer with View --> Command Palette --> Simple Browser: Show
-# 5. use the URL from prior steps as intput into this simple browser
+# put streamlit code here as needed
 
-
-import streamlit as st
+import pandas as pd
 import altair as alt
-from vega_datasets import data
- 
-st.title('Streamlit App for IS445: ID13029')
+import streamlit as st
 
-st.text("The URL for this app is: https://huggingface.co/spaces/kchou6/is445_demo")
+url = "https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/building_inventory.csv"
+data = pd.read_csv(url)
+# print(data.head())
 
-source = data.seattle_weather()
+st.title("Building Inventory Visualization")
+st.markdown("Displayed here are the data visuals from Building Inventory dataset.")
+st.write(data.head())
 
-scale = alt.Scale(
-    domain=["sun", "fog", "drizzle", "rain", "snow"],
-    range=["#e7ba52", "#a7a7a7", "#aec7e8", "#1f77b4", "#9467bd"],
+# Visualization 1: Buildings by Usage Description
+st.subheader("Buildings by Usage Description")
+usage_data = data['Usage Description'].value_counts().reset_index()
+usage_data.columns = ['Usage Description', 'Count']
+
+chart5 = alt.Chart(usage_data).mark_bar().encode(
+    x=alt.X('Count:Q', title='Number of Buildings'),
+    y=alt.Y('Usage Description:N', sort='-x', title='Usage Description'),
+    color=alt.Color('Usage Description:N', legend=None),
+    tooltip=['Usage Description', 'Count']
+).properties(
+    width=600,
+    height=400,
+    title="Buildings by Usage Description"
 )
-color = alt.Color("weather:N", scale=scale)
 
-# We create two selections:
-# - a brush that is active on the top panel
-# - a multi-click that is active on the bottom panel
-brush = alt.selection_interval(encodings=["x"])
-click = alt.selection_point(encodings=["color"])
+st.altair_chart(chart5, use_container_width=True)
 
-# Top panel is scatter plot of temperature vs time
-points = (
-    alt.Chart()
-    .mark_point()
-    .encode(
-        alt.X("monthdate(date):T", title="Date (Month Year)"),
-        alt.Y(
-            "temp_max:Q",
-            title="Maximum Daily Temperature (C)",
-            scale=alt.Scale(domain=[-5, 40]),
-        ),
-        color=alt.condition(brush, color, alt.value("lightgray")),
-        size=alt.Size("precipitation:Q", scale=alt.Scale(range=[5, 200])),
-    )
-    .properties(width=550, height=300)
-    .add_params(brush)
-    .transform_filter(click)
+# Write-up for Visualization 1
+st.write("Buildings by Usage Description")
+st.write(
+    "This bar chart represents the distribution of buildings based on their usage descriptions. "
+    "The x-axis displays the number of buildings, while the y-axis lists the various usage categories. "
+    "Each bar reflects the count of buildings in a specific usage type, with distinct colors to enhance readability and tooltips to show precise values."
+)
+st.write(
+    "I chose a bar chart because it effectively highlights the differences in building usage, making comparisons straightforward. "
+    "This visualization allows users to quickly identify the most common and least common usage types. "
+    "If I had more time, I would add advanced interactivity, such as a dropdown menu or filter, to explore specific usage categories or group similar types for deeper insights."
 )
 
-# Bottom panel is a bar chart of weather type
-bars = (
-    alt.Chart()
-    .mark_bar()
-    .encode(
-        x="count()",
-        y="weather:N",
-        color=alt.condition(click, color, alt.value("lightgray")),
-    )
-    .transform_filter(brush)
-    .properties(
-        width=550,
-    )
-    .add_params(click)
+
+# Visualization 2: Floor Count Distribution
+st.subheader("Distribution of Number of Floors")
+floor_count_data = data['Total Floors'].dropna().value_counts().reset_index()
+floor_count_data.columns = ['Total Floors', 'Count']
+
+chart4 = alt.Chart(floor_count_data).mark_bar().encode(
+    x=alt.X('Total Floors:O', title='Number of Floors'),
+    y=alt.Y('Count:Q', title='Number of Buildings'),
+    color=alt.Color('Total Floors:O', legend=None),
+    tooltip=['Total Floors', 'Count']
+).properties(
+    width=600,
+    height=400,
+    title="Distribution of Number of Floors in Buildings"
 )
 
-chart = alt.vconcat(points, bars, data=source, title="Seattle Weather - 2018 to 2027")
+st.altair_chart(chart4, use_container_width=True)
+
+
 
-tab1, tab2 = st.tabs(["Streamlit theme (default)", "Altair native theme"])
 
-with tab1:
-    st.altair_chart(chart, theme="streamlit", use_container_width=True)
-with tab2:
-    st.altair_chart(chart, theme=None, use_container_width=True)
+# Write-up for Visualization 2
+st.write("Distribution of Number of Floors")
+st.write(
+    "This bar chart visualizes the distribution of buildings based on the number of floors. "
+    "The x-axis represents the total number of floors, while the y-axis displays the count of buildings with that floor count. "
+    "Each bar is color-coded to improve visual appeal, and tooltips provide exact counts for added interactivity."
+)
+st.write(
+    "I chose a bar chart for this visualization because it effectively captures the distribution of floor counts and allows users to identify trends, "
+    "such as the most common floor counts. This visualization makes it easy to compare the data and spot unusual patterns. "
+    "If I had more time, I would incorporate filters or sliders to let users explore specific floor ranges or analyze the data further by other building attributes such as usage type or region."
+)

requirements.txt

@@ -1,3 +1,2 @@
-streamlit
+pandas
 altair
-vega_datasets