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Update src/streamlit_app.py
Browse files- src/streamlit_app.py +4 -2
src/streamlit_app.py
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@@ -8,8 +8,6 @@ Path(os.environ["STREAMLIT_CONFIG_DIR"]).mkdir(parents=True, exist_ok=True)
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import streamlit as st
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# …the rest of your imports and code…
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# Imports.
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import streamlit as st
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import seaborn as sns
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@@ -252,6 +250,10 @@ line_chart
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st.markdown(""" This plot is a line chart visualizing the annual number of incidents for the top 5 most frequent crime types over a five-year period, from 2020 to 2024. Each line represents a distinct crime type, allowing for easy comparison of trends across different categories. The x-axis represents the year, the y-axis indicates the number of incidents, and a legend identifies the color corresponding to each specific crime type: Battery - Simple Assault, Burglary From Vehicle, Theft of Identity, Vandalism - Felony , and Vehicle - Stolen. The plot highlights the fluctuations and overall trajectories of these major crime categories across the years.""")
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### Use this one!!!
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# Identify top 10 crime types
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top_10_crimes = df['crm_cd_desc'].value_counts().nlargest(10).index.tolist()
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import streamlit as st
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# …the rest of your imports and code…
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# Imports.
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import streamlit as st
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import seaborn as sns
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st.markdown(""" This plot is a line chart visualizing the annual number of incidents for the top 5 most frequent crime types over a five-year period, from 2020 to 2024. Each line represents a distinct crime type, allowing for easy comparison of trends across different categories. The x-axis represents the year, the y-axis indicates the number of incidents, and a legend identifies the color corresponding to each specific crime type: Battery - Simple Assault, Burglary From Vehicle, Theft of Identity, Vandalism - Felony , and Vehicle - Stolen. The plot highlights the fluctuations and overall trajectories of these major crime categories across the years.""")
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### Use this one!!!
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# Load data
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with open("County_Boundary.geojson", "r", encoding="utf-8") as f:
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geojson_data = json.load(f)
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# Identify top 10 crime types
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top_10_crimes = df['crm_cd_desc'].value_counts().nlargest(10).index.tolist()
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