code: def get_probs(data, prob_column=None):
      
      if prob_column is None:
          p = None
      else:
          p = data[prob_column].fillna(0).values
          if p.sum() == 0:
              p = np.ones(len(p))
          if abs(p.sum() - 1.0) > 1e-8:
              p = p / (1.0 * p.sum())
      return p
docs: Checks for presence of a probability column and returns the result
      as a numpy array. If the probabilities are weights (i.e. they don't
      sum to 1), then this will be recalculated.
  
      Parameters
      ----------
      data: pandas.DataFrame
          Table to sample from.
      prob_column: string, optional, default None
          Name of the column in the data to provide probabilities or weights.
  
      Returns
      -------
      numpy.array