Total Energy Map by Building - Building Type

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Get energy data and sum/chart over map area

    ### Use BDXpy here to call your buildings and load a gps file to match on building name #####
    ##  store variables in df in below code


    # Ensure your start and end dates are in datetime format
    start_date = pd.to_datetime('2024-09-01')
    end_date = pd.to_datetime('2024-09-30')

    # Filter the DataFrame
    filtered_df = df[(df['Timestamp'] >= start_date) & (df['Timestamp'] <= end_date)]
    filtered_df.to_csv("filtered_df.csv", index=False)

    # Define columns to keep without aggregation (taking the first occurrence)
    cols_to_keep = ["Building Name", "Building Type", "Latitude", "Longitude"]

    # Define columns to sum
    cols_to_sum = ["Total Energy", "Electric Energy", "Cooling Energy", "Heating Energy",
                "Total Cost", "Metric Tons CO2"]

    # Group by 'Building Name', summing only the relevant columns
    df_summed = filtered_df.groupby("Building Name", as_index=False).agg(
        {**{col: 'first' for col in cols_to_keep},  # Keep first occurrence
        **{col: 'sum' for col in cols_to_sum},  # Sum numeric values
        "Timestamp": "count"}  # Count total rows
    )

    # Rename the counted column
    df_summed = df_summed.rename(columns={"Timestamp": "Total Rows Summed"})
    df_summed.to_csv("df_summed.csv", index=False)

    # Create a map with bubble size variation and opacity
    fig = px.scatter_mapbox(
        df_summed,
        lat='Latitude',
        lon='Longitude',
        hover_name='Building Name',
        size='Total Energy',  # Bubble size based on Total Energy
        color='Building Type',  # Color based on Building Type
        size_max=40,  # Max bubble size
        zoom=14,
        mapbox_style="carto-positron",
        text='Building Name', 
        opacity = 0.6,
        title= "Energy Consumption by Building Type"
    )

    # Set map layout
    fig.update_layout(
        mapbox_center={"lat": 51.7540, "lon": -1.2577},
        margin={"r": 10, "t": 100, "l": 10, "b": 10},
        height=800  # Adjust height for better layout
    )

    fig.update_traces(marker=dict(opacity=0.6))

    fig.show()
    print(fig.data[0])


    # Example usage
    fig.write_html("mapenergychart.html")