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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 ssad(patch, cols, splits):
 """ Calculates an empirical intra-specific spatial abundance distribution Parameters {0} Returns ------- {1} Result has one column giving the individuals of species in each subplot. Notes ----- {2} {3} Examples -------- {4} y 0 42 1 20 2 60 3 60 4 88 5 86 6 20 7 0 8 110 9 12 10 115 11 180 12 160 13 120 14 26 15 11 See http://www.macroeco.org/tutorial_macroeco.html for additional examples and explanation """


    # Get and check SAD
    sad_results = sad(patch, cols, splits, clean=False)

    # Create dataframe with col for spp name and numbered col for each split
    for i, sad_result in enumerate(sad_results):
        if i == 0:  # For first result, create dataframe
            fulldf = sad_result[1]
            fulldf.columns = ['spp', '0']  # Renames y col to 0
        else:  # For other results, append col to dataframe, named by num
            fulldf[str(i)] = sad_result[1]['y']

    # Get each spp SSAD (row of fulldf) and append as tuple in result_list
    result_list = []
    for _, row in fulldf.iterrows():
        row_values_array = np.array(row[1:], dtype=float)
        result_list.append((row[0], pd.DataFrame({'y': row_values_array})))

    # Return all results
    return result_list