afa/app/app.py [1048:1072]:
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    df_grp = df_grp_demand \
        .merge(df_grp_metrics, on=groupby_cols, how="left") \
        .sort_values(by="demand", ascending=False)
    df_grp["cperc"] = df_grp["perc"].cumsum()
    df_grp = df_grp.query(f"cperc <= {cperc_thresh}")
    df_grp.rename({"perc": "% total demand", "accuracy": "% accuracy"}, axis=1, inplace=True)
    df_grp.drop("cperc", axis=1, inplace=True)

    # calc. summary row
    df_grp_summary = df_grp.agg({"demand": sum, "% accuracy": np.nanmean})

    df_grp_summary["% total demand"] = np.round(100 * df_grp_summary["demand"] / total_demand, 1)
    df_grp_summary = pd.DataFrame(df_grp_summary).T[["demand", "% total demand", "% accuracy"]]
    df_grp_summary.insert(0, "group by", ", ".join(groupby_cols))
    df_grp_summary["% accuracy"] = df_grp_summary["% accuracy"].round(0)

    df_grp["demand"] = df_grp["demand"].round(0)
    df_grp["% total demand"] = df_grp["% total demand"].round(1)
    df_grp["% accuracy"] = df_grp["% accuracy"].round(0)
    df_grp.insert(0, "rank", np.arange(df_grp.shape[0]) + 1)

    df_grp_summary["demand"] = df_grp_summary["demand"].round(0)
    df_grp_summary["% total demand"] = df_grp_summary["% total demand"].round(1)
    
    return df_grp, df_grp_summary
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afa/app/app.py [1123:1147]:
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    df_grp = df_grp_demand \
        .merge(df_grp_metrics, on=groupby_cols, how="left") \
        .sort_values(by="demand", ascending=False)
    df_grp["cperc"] = df_grp["perc"].cumsum()
    df_grp = df_grp.query(f"cperc <= {cperc_thresh}")
    df_grp.rename({"perc": "% total demand", "accuracy": "% accuracy"}, axis=1, inplace=True)
    df_grp.drop("cperc", axis=1, inplace=True)

    # calc. summary row
    df_grp_summary = df_grp.agg({"demand": sum, "% accuracy": np.nanmean})

    df_grp_summary["% total demand"] = np.round(100 * df_grp_summary["demand"] / total_demand, 1)
    df_grp_summary = pd.DataFrame(df_grp_summary).T[["demand", "% total demand", "% accuracy"]]
    df_grp_summary.insert(0, "group by", ", ".join(groupby_cols))
    df_grp_summary["% accuracy"] = df_grp_summary["% accuracy"].round(0)

    df_grp["demand"] = df_grp["demand"].round(0)
    df_grp["% total demand"] = df_grp["% total demand"].round(1)
    df_grp["% accuracy"] = df_grp["% accuracy"].round(0)
    df_grp.insert(0, "rank", np.arange(df_grp.shape[0]) + 1)

    df_grp_summary["demand"] = df_grp_summary["demand"].round(0)
    df_grp_summary["% total demand"] = df_grp_summary["% total demand"].round(1)
    
    return df_grp, df_grp_summary
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