old_contents: import numpy as np
  import scipy.sparse as sp
  from numpy.testing import assert_array_almost_equal
  
  from sklearn.datasets import make_classification
  from sklearn.utils.sparsefuncs import mean_variance_axis0
  
  
  def test_mean_variance_axis0():
      X, _ = make_classification(5, 4, random_state=0)
      # Sparsify the array a little bit
      X[0, 0] = 0
      X[2, 1] = 0
      X[4, 3] = 0
      X_csr = sp.csr_matrix(X)
      X_csr[1, 0] = 0
      X[1, 0] = 0
      X_means, X_vars = mean_variance_axis0(X_csr)
      assert_array_almost_equal(X_means, np.mean(X, axis=0))
      assert_array_almost_equal(X_vars, np.var(X, axis=0))
  
      X_csc = sp.csc_matrix(X)
      X_csc[1, 0] = 0
      X[1, 0] = 0
      X_means, X_vars = mean_variance_axis0(X_csc)
  
      assert_array_almost_equal(X_means, np.mean(X, axis=0))
      assert_array_almost_equal(X_vars, np.var(X, axis=0))
  
new_contents: import numpy as np
  import scipy.sparse as sp
  from numpy.testing import assert_array_almost_equal
  
  from sklearn.datasets import make_classification
  from sklearn.utils.sparsefuncs import mean_variance_axis0
  
  
  def test_mean_variance_axis0():
      X, _ = make_classification(5, 4, random_state=0)
      # Sparsify the array a little bit
      X[0, 0] = 0
      X[2, 1] = 0
      X[4, 3] = 0
      X_lil = sp.lil_matrix(X)
      X_lil[1, 0] = 0
      X[1, 0] = 0
      X_csr = sp.csr_matrix(X_lil)
  
      X_means, X_vars = mean_variance_axis0(X_csr)
      assert_array_almost_equal(X_means, np.mean(X, axis=0))
      assert_array_almost_equal(X_vars, np.var(X, axis=0))
  
      X_csc = sp.csc_matrix(X_lil)
      X_means, X_vars = mean_variance_axis0(X_csc)
  
      assert_array_almost_equal(X_means, np.mean(X, axis=0))
      assert_array_almost_equal(X_vars, np.var(X, axis=0))
  
subject: Fix warning.