code: def uniform_numeric_column(
      n, col_type=float, range=(0, 1), missingness=0.0
  ):
      """
      Return an SArray of uniformly random numeric values.
  
      Parameters
      ----------
      n : int
          Number of entries in the output SArray.
  
      col_type : type, optional
          Type of the output SArray. Default is floats.
  
      range : tuple[int, int], optional
          Minimum and maximum of the uniform distribution from which values are
          chosen.
  
      missingness : float, optional
          Probability that a given entry in the output is missing.
  
      Returns
      -------
      out : SArray
      """
      if col_type == int:
          v = np.random.randint(
              low=range[0], high=range[1], size=n
          ).astype(float)
      else:
          v = np.random.rand(n)
          v = v * (range[1] - range[0]) + range[0]
  
      idx_na = np.random.rand(n) < missingness
      v[idx_na] = None
      v = np.where(np.isnan(v), None, v)
  
      return SArray(v, dtype=col_type)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import random
  import tempfile
  import shutil
  import math
  import string
  import numpy
  from pandas.util.testing import assert_frame_equal
  from ..None import SArray
  import turicreate
  import sys
  from six import StringIO
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: uniform_numeric_column(n, col_type, range, missingness) and returns: SArray