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| # put streamlit code here as needed | |
| import pandas as pd | |
| import altair as alt | |
| import streamlit as st | |
| url = "https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/building_inventory.csv" | |
| data = pd.read_csv(url) | |
| # print(data.head()) | |
| st.title("Building Inventory Visualization") | |
| st.markdown("Displayed here are the data visuals from Building Inventory dataset.") | |
| st.write(data.head()) | |
| # 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" | |
| ) | |
| st.altair_chart(chart5, use_container_width=True) | |
| # 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." | |
| ) | |
| # 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" | |
| ) | |
| st.altair_chart(chart4, 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." | |
| ) | |