def METHOD_NAME(df_mech: pd.DataFrame) -> pd.DataFrame:
    """
    Create the homicides by mechanism dataframe where we will have  homicides/homicide rate
    disaggregated by mechanism (e.g. weapon)
    """
    # df_mech = df_mech.drop(columns=["region", "subregion", "indicator", "dimension", "source", "sex", "age"])
    df_mech = df_mech.copy(deep=True)
    df_mech["category"] = (
        df_mech["category"]
        .map({"Firearms or explosives - firearms": "Firearms", "Another weapon - sharp object": "Sharp object"})
        .fillna(df_mech["category"])
    )
    # Make the table wider so we have a column for each mechanism
    df_mech = pivot_and_format_df(
        df_mech,
        drop_columns=["region", "subregion", "indicator", "dimension", "source", "sex", "age"],
        pivot_index=["country", "year"],
        pivot_values=["value"],
        pivot_columns=["category", "unit_of_measurement"],
    )
    return df_mech