27516f9a7f48a540fd33354b92fde425885fdc14,pudl/outputs.py,,generators_eia860,#Any#,370

Before Change

    out_df = out_df.drop(cols_to_drop, axis=1)

    // Re-arrange the dataframe to be more readable:
    out_df = out_df[[
        "report_year",
        "operator_id",
        "util_id_pudl",
        "operator_name",
        "plant_id",
        "plant_id_pudl",
        "plant_name",
        "generator_id",
        "state",
        "county",
        "latitude",
        "longitude",
        "prime_mover",
        "unit_code",
        "status",
        "ownership",
        "duct_burners",
        "nameplate_capacity_mw",
        "summer_capacity_mw",
        "winter_capacity_mw",
        "operating_date",
        "energy_source_1",
        "energy_source_2",
        "energy_source_3",
        "energy_source_4",
        "energy_source_5",
        "energy_source_6",
        "multiple_fuels",
        "deliver_power_transgrid",
        "syncronized_transmission_grid",
        "turbines",
        "cogenerator",
        "sector_name",
        "sector",
        "topping_bottoming",
        "planned_modifications",
        "planned_net_summer_capacity_uprate",
        "planned_net_winter_capacity_uprate",
        "planned_uprate_date",
        "planned_net_summer_capacity_derate",
        "planned_net_winter_capacity_derate",
        "planned_derate_date",
        "planned_new_prime_mover",
        "planned_energy_source_1",
        "planned_repower_date",
        "other_planned_modifications",
        "other_modifications_date",
        "planned_retirement_date",
        "solid_fuel_gasification",
        "pulverized_coal_tech",
        "fluidized_bed_tech",
        "subcritical_tech",
        "supercritical_tech",
        "ultrasupercritical_tech",
        "carbon_capture",
        "startup_source_1",
        "startup_source_2",
        "startup_source_3",
        "startup_source_4",
        "technology",
        "turbines_inverters_hydrokinetics",
        "time_cold_shutdown_full_load",
        "stoker_tech",
        "other_combustion_tech",
        "planned_new_nameplate_capacity_mw",
        "cofire_fuels",
        "switch_oil_gas",
        "heat_bypass_recovery",
        "rto_iso_lmp_node",
        "rto_iso_location_wholesale_reporting",
        "nameplate_power_factor",
        "minimum_load_mw",
        "uprate_derate_during_year",
        "uprate_derate_completed_date",
        "associated_combined_heat_power",
        "original_planned_operating_date",
        "current_planned_operating_date",
        "summer_estimated_capability",
        "winter_estimated_capability",
        "operating_switch",
        "previously_canceled",
        "retirement_date",
    ]]

    return(out_df)

After Change

    cols_to_drop = ["id", ]
    out_df = out_df.drop(cols_to_drop, axis=1)

    first_cols = [
        "report_year",
        "plant_id",
        "plant_id_pudl",
        "plant_name",
        "operator_id",
        "util_id_pudl",
        "operator_name",
        "generator_id",
    ]

    // Re-arrange the columns for easier readability:
    out_df = organize_cols(out_df, first_cols)

    return(out_df)

Instances