FantasticTony commited on
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1 Parent(s): 77f1587

format text

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  1. app.py +19 -7
app.py CHANGED
@@ -54,17 +54,29 @@ chart2 = alt.Chart(license_status_counts).mark_line().encode(
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  st.title('Licenses Data Visualization')
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  st.write("## Introduction")
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- st.text(
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- "This app provides visual insights into licensing data by focusing on the distribution of license types and tracking the trends of license statuses over time. The visualizations aim to simplify the exploration of the dataset and highlight key insights into how licenses are distributed and change year over year.")
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-
 
 
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  st.write("## Visualization 1: Distribution of License Types")
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  st.altair_chart(chart1, use_container_width=True)
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- st.text(
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- "This bar chart shows the distribution of the top 10 license types in the dataset. I chose a bar chart because it is effective for comparing categorical data like license types. The colors help to visually differentiate each category, and focusing on the top 10 types makes the chart more readable and less cluttered. If I had more time, I would add interactive tooltips to display additional information about each license type, such as the percentage of the total.")
 
 
 
 
 
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  st.write("## Visualization 2: License Status Over Time")
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  st.altair_chart(chart2, use_container_width=True)
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- st.text(
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- "This line chart shows the number of licenses by key statuses over time. The chart helps to visualize trends in the renewal and activity status of licenses. I chose different colors for each status to make it easy to distinguish between them. Filtering for key statuses allows for a clearer view of the most important trends without overwhelming the viewer. If I had more time, I would include an option to filter by license type to see how specific types have changed over time.")
 
 
 
 
 
 
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  st.title('Licenses Data Visualization')
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  st.write("## Introduction")
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+ st.text("""
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+ This app provides visual insights into licensing data by focusing on the distribution of license types and
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+ tracking the trends of license statuses over time. The visualizations aim to simplify the exploration
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+ of the dataset and highlight key insights into how licenses are distributed and change year over year.
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+ """)
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  st.write("## Visualization 1: Distribution of License Types")
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  st.altair_chart(chart1, use_container_width=True)
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+ st.text("""
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+ This bar chart shows the distribution of the top 10 license types in the dataset. I chose a bar chart
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+ because it is effective for comparing categorical data like license types. The colors help to visually
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+ differentiate each category, and focusing on the top 10 types makes the chart more readable and
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+ less cluttered. If I had more time, I would add interactive tooltips to display additional
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+ information about each license type, such as the percentage of the total.
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+ """)
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  st.write("## Visualization 2: License Status Over Time")
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  st.altair_chart(chart2, use_container_width=True)
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+ st.text("""
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+ This line chart shows the number of licenses by key statuses over time.
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+ The chart helps to visualize trends in the renewal and activity status of licenses. I chose different colors
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+ for each status to make it easy to distinguish between them. Filtering for key statuses allows for a clearer
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+ view of the most important trends without overwhelming the viewer. If I had more time, I would include an option
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+ to filter by license type to see how specific types have changed over time.
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+ """)