Delete app.py

app.py

@@ -1,113 +0,0 @@
-import streamlit as st
-import pandas as pd
-import altair as alt
-
-# Loading the dataset
-url = 'https://github.com/UIUC-iSchool-DataViz/is445_data/raw/main/licenses_fall2022.csv'
-df = pd.read_csv(url)
-
-# Dropping unnecessary columns to prevent serialization errors and reduce data size
-df.drop(columns=['Title', 'Prefix', 'Suffix', 'BusinessDBA', '_id', 'Delegated Controlled Substance Schedule', 'Case Number'], inplace=True, errors='ignore')
-
-# Converting date columns to datetime format, handling errors
-date_columns = ['Original Issue Date', 'Effective Date', 'Expiration Date', 'LastModifiedDate']
-for col in date_columns:
-    df[col] = pd.to_datetime(df[col], errors='coerce')
-
-# Converting object columns to category for performance improvement
-for col in df.select_dtypes(include='object').columns:
-    df[col] = df[col].astype('category')
-
-# Dropping rows with missing values in essential columns to ensure Arrow compatibility
-df.dropna(subset=['License Type', 'License Status', 'Original Issue Date'], inplace=True)
-
-# Adding Year columns for visualizations
-df['Original Issue Year'] = df['Original Issue Date'].dt.year
-
-# Visualization 1: Bar chart showing licenses by Status
-st.subheader("Licenses by Status")
-category_counts = df['License Status'].value_counts().reset_index()
-category_counts.columns = ['License Status', 'Count']
-
-if not category_counts.empty:
-    chart1 = alt.Chart(category_counts).mark_bar().encode(
-        x=alt.X('License Status', sort='-y', title='License Type'),
-        y=alt.Y('Count', title='Number of Licenses', scale=alt.Scale(domain=[0, 100])),
-        color=alt.Color('License Status', legend=None)
-    ).properties(
-        width=600,
-        height=400,
-        title="Number of Licenses by Status"
-    )
-    st.altair_chart(chart1)
-else:
-    st.write("No data available for the 'License Status' plot.")
-
-# Write-up for Visualization 1
-st.write("""
-**Licenses by Status**: This bar chart visualizes the distribution of licenses by status, helping to identify the prevalence of each category.
-The use of a bar chart allows for a straightforward comparison between different statuses. Sorting the bars in descending order aids in readability, 
-while color differentiation helps make each category distinct. Setting the Y-axis scale to a maximum of 100 highlights categories with smaller counts, 
-giving a balanced view of the data distribution. This scale choice emphasizes categories with relatively lower counts, which might otherwise be overshadowed. 
-If given more time, interactive features such as filtering by other license attributes would add to the depth of analysis.
-""")
-
-# Visualization 2: Line chart showing licenses issued over time
-st.subheader("Licenses Over Time")
-time_data = df.groupby('Original Issue Year').size().reset_index(name='License Count')
-
-if not time_data.empty:
-    chart2 = alt.Chart(time_data).mark_line().encode(
-        x=alt.X('Original Issue Year', title='Year'),
-        y=alt.Y('License Count', title='Number of Licenses', scale=alt.Scale(domain=[0, 100])),
-        color=alt.value('blue')
-    ).properties(
-        width=600,
-        height=400,
-        title="Number of Licenses Issued Over Time"
-    )
-    st.altair_chart(chart2)
-else:
-    st.write("No data available for the 'Licenses Over Time' plot.")
-
-# Write-up for Visualization 2
-st.write("""
-**Licenses Over Time**: This line chart tracks the trend of licenses issued annually, illustrating changes in licensing activity over time.
-Using a line chart effectively displays year-over-year fluctuations, making it easy to observe trends. The color choice of blue adds consistency and clarity.
-Setting the Y-axis to a maximum scale of 100 allows smaller annual variations to be more visible, offering a clearer picture of subtle changes in license issuance.
-With more development time, analyzing seasonal or monthly trends within each year could yield additional insights, revealing potential cyclical patterns.
-""")
-
-# Write-up explaining the thought process for both visualisations 
-st.write("""
-**Write-up explaining the thought process for both visualisations:**
-
-** 1. Licenses by Status:**
-
-Purpose: This bar chart compares the frequency of different license statuses, giving insight into how licenses are categorized. For example, it might show the relative proportion of active versus inactive licenses.
-
-Design Choices:
-
-Sorting bars in descending order brings the most common categories to the top, allowing for quick comparison.
-
-Colors are used for clear category distinction, making the chart visually engaging.
-
-A fixed maximum of 100 on the Y-axis balances the focus on both high and low-count categories, helping smaller counts stand out.
-
-Future Enhancements: Interactivity, such as the ability to filter by additional fields like specialty or location, could enhance insights further.
-
-
-** 2. Licenses Over Time:**
-
-Purpose: This line chart provides a time-based view, revealing trends in license issuance across years. It is especially useful for identifying growth or decline patterns in licensing.
-
-Design Choices:
-
-The line chart captures year-over-year changes smoothly, helping viewers spot peaks and troughs.
-
-Using a single color (blue) maintains simplicity and avoids distractions from the primary focus on the trend.
-
-Setting the Y-axis to 100 highlights variations even when counts are low, helping identify periods of slow growth or decline.
-
-Future Enhancements: Examining more granular patterns, such as seasonal trends, could add another dimension, revealing factors that might influence licensing activity annually.
-""")