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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 ks_summary(pareto_tail_indices):
 """Display a summary of Pareto tail indices. Parameters pareto_tail_indices : array Pareto tail indices. Returns ------- df_k : dataframe Dataframe containing k diagnostic values. """

    kcounts, _ = np.histogram(pareto_tail_indices, bins=[-np.Inf, 0.5, 0.7, 1, np.Inf])
    kprop = kcounts / len(pareto_tail_indices) * 100
    df_k = pd.DataFrame(
        dict(_=["(good)", "(ok)", "(bad)", "(very bad)"], Count=kcounts, Pct=kprop)
    ).rename(index={0: "(-Inf, 0.5]", 1: " (0.5, 0.7]", 2: "   (0.7, 1]", 3: "   (1, Inf)"})

    if np.sum(kcounts[1:]) == 0:
        warnings.warn("All Pareto k estimates are good (k < 0.5)")
    elif np.sum(kcounts[2:]) == 0:
        warnings.warn("All Pareto k estimates are ok (k < 0.7)")

    return df_k