code: def test_micedata_miss1():
      # test for #4375
      gen = np.random.RandomState(3897)
      data = pd.DataFrame(gen.rand(50, 4))
      data.columns = ["var1", "var2", "var3", "var4"]
      # one column with a single missing value
      data.iloc[1, 1] = np.nan
      data.iloc[[1, 3], 2] = np.nan
  
      data_imp = mice.MICEData(data)
      data_imp.update_all()
  
      assert_equal(data_imp.data.isnull().values.sum(), 0)
  
      ix_miss = {
          "var1": np.array([], dtype=np.int64),
          "var2": np.array([1], dtype=np.int64),
          "var3": np.array([1, 3], dtype=np.int64),
          "var4": np.array([], dtype=np.int64),
      }
  
      for k in ix_miss:
          assert_equal(data_imp.ix_miss[k], ix_miss[k])
  
imports: import numpy
  import pandas
  import pytest
  from statsmodels.imputation import mice
  import statsmodels.api
  from numpy.testing import assert_equal, assert_allclose
  import warnings
label_desc: Use image node_id 4 for calling a global function with example usage: test_micedata_miss1() without return types