text: import platform
  import numpy as np
  import vaex
  import pytest
  import sys
  
  
  version = tuple(map(int, np.__version__.split('.')))
  
  @pytest.mark.skipif(platform.system().lower() == 'windows', reason="strange ref count issue with numpy")
  def test_percentile_approx():
      df = vaex.example()
      # Simple test
      percentile = df.percentile_approx('z', percentage=99)
      expected_result = 15.1739
      np.testing.assert_almost_equal(percentile, expected_result, decimal=1)
  
      # Test for multiple percentages
      percentiles = df.percentile_approx('x', percentage=[0, 25, 50, 75, 100], percentile_shape=65536)
      expected_result = [-78.133026, -3.5992, -0.0367, 3.4684, 130.49751]
      np.testing.assert_array_almost_equal(percentiles, expected_result, decimal=1)
  
      # Test for multiple expressions
      percentiles_2d = df.percentile_approx(['x', 'y'], percentage=[33, 66])
      expected_result = np.array(([-2.3310, 1.9540], [-2.4313, 2.1021]))
      np.testing.assert_array_almost_equal(percentiles_2d, expected_result, decimal=1)
  
  
  @pytest.mark.skipif(platform.system().lower() == 'windows', reason="strange ref count issue with numpy")
  def test_percentile_1d():
      x = np.array([0, 0, 10, 100, 200])
      df = vaex.from_arrays(x=x)
      median = df.median_approx(df.x)
      assert median < 10.
  
      x = np.array([0, 0, 90, 100, 200])
      df = vaex.from_arrays(x=x)
      median = df.median_approx(df.x)
      assert median > 90.
  
      # coverage test
      df = vaex.example()
      df.percentile_approx('x', percentage=80, binby=df.z, limits='minmax', shape=100)
  