code: def toPandas(self, df):
          
          cols = df.columns
          # Convert any MLlib Vector columns to scipy.sparse.csr_matrix
          matrixCols = []
  
          def toscipy(v):
              if isinstance(v, DenseVector):
                  return csr_matrix((v.values, np.array(range(v.size)), np.array([0, v.size])),
                                    shape=(1, v.size))
              elif isinstance(v, SparseVector):
                  return csr_matrix((v.values, v.indices, np.array([0, len(v.indices)])),
                                    shape=(1, v.size))
              else:
                  raise TypeError("Converter.toPandas found unknown Vector type: %s" % type(v))
          tosparse = udf(lambda v: toscipy(v), CSRVectorUDT())
          for i in range(len(cols)):
              c = cols[i]
              if isinstance(df.schema.fields[i].dataType, VectorUDT):
                  cols[i] = tosparse(df[c]).alias(c)
                  matrixCols.append(c)
              else:
                  cols[i] = df[c]
          return df.select(*cols).toPandas()
docs: This is similar to the Spark DataFrame built-in toPandas() method, but it handles
          MLlib Vector columns differently.  It converts MLlib Vectors into rows of
          scipy.sparse.csr_matrix, which is generally friendlier for PyData tools like scikit-learn.
  
          .. note:: Experimental: This will likely be replaced in later releases with improved APIs.
  
          :param df: Spark DataFrame
          :return:  Pandas dataframe