30703a7036f9bfd58ba23c2872887f1820ef928e,pudl/outputs.py,,fuel_ferc1_df,#Any#,94

Before Change

    // Build a SELECT statement that gives us information from several different
    // tables that are relevant to FERC Fuel.
    fuel_ferc1_select = sa.sql.select([
        pt["fuel_ferc1"].c.report_year,
        pt["utilities_ferc"].c.respondent_id,
        pt["utilities_ferc"].c.respondent_name,
        pt["utilities_ferc"].c.util_id_pudl,
        pt["plants_ferc"].c.plant_id_pudl,
        pt["fuel_ferc1"].c.plant_name,
        pt["fuel_ferc1"].c.fuel,
        pt["fuel_ferc1"].c.fuel_qty_burned,
        pt["fuel_ferc1"].c.fuel_avg_mmbtu_per_unit,
        pt["fuel_ferc1"].c.fuel_cost_per_unit_burned,
        pt["fuel_ferc1"].c.fuel_cost_per_unit_delivered,
        pt["fuel_ferc1"].c.fuel_cost_per_mmbtu,
        pt["fuel_ferc1"].c.fuel_cost_per_mwh,
        pt["fuel_ferc1"].c.fuel_mmbtu_per_mwh]).\
        where(sa.sql.and_(
            pt["utilities_ferc"].c.respondent_id ==
            pt["fuel_ferc1"].c.respondent_id,
            pt["plants_ferc"].c.respondent_id ==
            pt["fuel_ferc1"].c.respondent_id,
            pt["plants_ferc"].c.plant_name ==
            pt["fuel_ferc1"].c.plant_name))

    // Pull the data from the DB into a DataFrame
    fuel_df = pd.read_sql(fuel_ferc1_select, pudl_engine)

After Change

    // Grab the list of tables so we can reference them shorthand.
    pt = models.PUDLBase.metadata.tables
    fuel_ferc1_tbl = pt["fuel_ferc1"]
    fuel_ferc1_select = sa.sql.select([fuel_ferc1_tbl, ])
    fuel_df = pd.read_sql(fuel_ferc1_select, pudl_engine)

    // We have two different ways of assessing the total cost of fuel given cost
    // per unit delivered and cost per mmbtu. They *should* be the same, but we
    // know they aren"t always. Calculate both so we can compare both.
    fuel_df["fuel_consumed_total_mmbtu"] = \
        fuel_df["fuel_qty_burned"] * fuel_df["fuel_avg_mmbtu_per_unit"]
    fuel_df["fuel_consumed_total_cost_mmbtu"] = \
        fuel_df["fuel_cost_per_mmbtu"] * fuel_df["fuel_consumed_total_mmbtu"]
    fuel_df["fuel_consumed_total_cost_unit"] = \
        fuel_df["fuel_cost_per_unit_burned"] * fuel_df["fuel_qty_burned"]

    pu_ferc = plants_utils_ferc_df(pudl_engine)

    out_df = pd.merge(fuel_df, pu_ferc, on=["respondent_id", "plant_name"])
    out_df = out_df.drop("id", axis=1)

    out_df = out_df[[
        "report_year",
        "respondent_id",
        "respondent_name",
        "util_id_pudl",
        "plant_name",
        "plant_id_pudl",
        "fuel",
        "fuel_unit",
        "fuel_qty_burned",
        "fuel_avg_mmbtu_per_unit",
        "fuel_cost_per_unit_burned",
        "fuel_cost_per_unit_delivered",
        "fuel_cost_per_mmbtu",
        "fuel_cost_per_mwh",
        "fuel_mmbtu_per_mwh",
        "fuel_consumed_total_mmbtu",
        "fuel_cost_per_mmbtu",
        "fuel_cost_per_unit_burned",
    ]]

    return(out_df)

Instances