<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def generate_component_id_namespace_overview(model, components):
 """ Tabulate which MIRIAM databases the component's identifier matches. Parameters model : cobra.Model A cobrapy metabolic model. components : {"metabolites", "reactions", "genes"} A string denoting `cobra.Model` components. Returns ------- pandas.DataFrame The index of the table is given by the component identifiers. Each column corresponds to one MIRIAM database and a Boolean entry determines whether the annotation matches. """

    patterns = {
        "metabolites": METABOLITE_ANNOTATIONS,
        "reactions": REACTION_ANNOTATIONS,
        "genes": GENE_PRODUCT_ANNOTATIONS
    }[components]
    databases = list(patterns)
    data = list()
    index = list()
    for elem in getattr(model, components):
        index.append(elem.id)
        data.append(tuple(patterns[db].match(elem.id) is not None
                          for db in databases))
    df = pd.DataFrame(data, index=index, columns=databases)
    if components != "genes":
        # Clean up of the dataframe. Unfortunately the Biocyc patterns match
        # broadly. Hence, whenever a Metabolite or Reaction ID matches to any
        # DB pattern AND the Biocyc pattern we have to assume that this is a
        # false positive.
        # First determine all rows in which 'biocyc' and other entries are
        # True simultaneously and use this Boolean series to create another
        # column temporarily.
        df['duplicate'] = df[df['biocyc']].sum(axis=1) >= 2
        # Replace all nan values with False
        df['duplicate'].fillna(False, inplace=True)
        # Use the additional column to index the original dataframe to identify
        # false positive biocyc hits and set them to False.
        df.loc[df['duplicate'], 'biocyc'] = False
        # Delete the additional column
        del df['duplicate']
    return df