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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def verified_funds(pronac, dt):
 """ Responsable for detecting anomalies in projects total verified funds. """

    dataframe = data.planilha_comprovacao
    project = dataframe.loc[dataframe['PRONAC'] == pronac]
    segment_id = project.iloc[0]["idSegmento"]
    pronac_funds = project[
        ["idPlanilhaAprovacao", "PRONAC", "vlComprovacao", "idSegmento"]
    ]
    funds_grp = pronac_funds.drop(columns=["idPlanilhaAprovacao"]).groupby(
        ["PRONAC"]
    )
    project_funds = funds_grp.sum().loc[pronac]["vlComprovacao"]

    segments_info = data.verified_funds_by_segment_agg.to_dict(orient="index")
    mean = segments_info[segment_id]["mean"]
    std = segments_info[segment_id]["std"]
    is_outlier = gaussian_outlier.is_outlier(project_funds, mean, std)
    maximum_expected_funds = gaussian_outlier.maximum_expected_value(mean, std)
    return {
            "is_outlier": is_outlier,
            "valor": project_funds,
            "maximo_esperado": maximum_expected_funds,
            "minimo_esperado": 0,
    }