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# 📋 Show Filtered Data Table
st.subheader("📋 Pole Data Table")
st.dataframe(filtered_df)
# 🔋 Bar Chart - Power Generation
st.subheader("🔋 Power Generation per Pole")
fig_bar = px.bar(
filtered_df,
x='Name',
y='Power_Generation__c',
color='Status__c',
labels={'Power_Generation__c': 'Power Generated (kWh)'},
title="Power Output by Pole",
height=400
)
st.plotly_chart(fig_bar, use_container_width=True)
# ⚠️ Pie Chart - Fault Status Distribution
st.subheader("⚠️ Fault Status Distribution")
fault_data = filtered_df['Fault_Status__c'].value_counts().reset_index()
fault_data.columns = ['Fault Status', 'Count']
fig_pie = px.pie(
fault_data,
names='Fault Status',
values='Count',
title="Fault Breakdown",
height=400
)
st.plotly_chart(fig_pie, use_container_width=True)
# 🕒 Line Chart - Pole Installations Over Time
st.subheader("🕒 Installation Trend")
install_trend = filtered_df.groupby(filtered_df['Installed_Date__c'].dt.to_period('M')).size()
install_trend.index = install_trend.index.to_timestamp()
fig_line_install = px.line(
x=install_trend.index,
y=install_trend.values,
labels={"x": "Month", "y": "Poles Installed"},
title="Pole Installations Over Time",
markers=True
)
st.plotly_chart(fig_line_install, use_container_width=True)
# 🛠️ Line Chart - Maintenance Activity
st.subheader("🛠️ Maintenance Trend")
maintenance_trend = filtered_df.groupby(filtered_df['Last_Maintenance_Date__c'].dt.to_period('M')).size()
maintenance_trend.index = maintenance_trend.index.to_timestamp()
fig_line_maint = px.line(
x=maintenance_trend.index,
y=maintenance_trend.values,
labels={"x": "Month", "y": "Maintenance Events"},
title="Maintenance Activities Over Time",
markers=True
)
st.plotly_chart(fig_line_maint, use_container_width=True)
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