old_contents: import dask.dataframe as dd
  import pandas.util.testing as tm
  import pandas as pd
  import dask
  dask.set_options(get=dask.async.get_sync)
  
  
  def test_make_timeseries():
      df = dd.demo.make_timeseries('2000', '2015', {'A': float, 'B': int, 'C': str},
                                   freq='2D', partition_freq='6M')
  
      assert df.divisions[0] == pd.Timestamp('2000-01-31', offset='6M')
      assert df.divisions[-1] == pd.Timestamp('2014-07-31', offset='6M')
      assert df.columns == ('A', 'B', 'C')
      assert df['A'].head().dtype == float
      assert df['B'].head().dtype == int
      assert df['C'].head().dtype == object
      assert df.divisions == tuple(pd.DatetimeIndex(start='2000', end='2015',
                                                    freq='6M'))
  
      tm.assert_frame_equal(df.head(), df.head())
  
      a = dd.demo.make_timeseries('2000', '2015', {'A': float, 'B': int, 'C': str},
                                   freq='2D', partition_freq='6M', seed=123)
      b = dd.demo.make_timeseries('2000', '2015', {'A': float, 'B': int, 'C': str},
                                   freq='2D', partition_freq='6M', seed=123)
      tm.assert_frame_equal(a.head(), b.head())
  
  
  def test_no_overlaps():
      df = dd.demo.make_timeseries('2000', '2001', {'A': float},
                                   freq='3H', partition_freq='3M')
  
      assert all(df.get_division(i).index.max().compute()
               < df.get_division(i + 1).index.min().compute()
               for i in range(df.npartitions - 2))
  
new_contents: import dask.dataframe as dd
  import pandas.util.testing as tm
  import pandas as pd
  
  
  def test_make_timeseries():
      df = dd.demo.make_timeseries('2000', '2015', {'A': float, 'B': int, 'C': str},
                                   freq='2D', partition_freq='6M')
  
      assert df.divisions[0] == pd.Timestamp('2000-01-31', offset='6M')
      assert df.divisions[-1] == pd.Timestamp('2014-07-31', offset='6M')
      assert df.columns == ('A', 'B', 'C')
      assert df['A'].head().dtype == float
      assert df['B'].head().dtype == int
      assert df['C'].head().dtype == object
      assert df.divisions == tuple(pd.DatetimeIndex(start='2000', end='2015',
                                                    freq='6M'))
  
      tm.assert_frame_equal(df.head(), df.head())
  
      a = dd.demo.make_timeseries('2000', '2015', {'A': float, 'B': int, 'C': str},
                                   freq='2D', partition_freq='6M', seed=123)
      b = dd.demo.make_timeseries('2000', '2015', {'A': float, 'B': int, 'C': str},
                                   freq='2D', partition_freq='6M', seed=123)
      tm.assert_frame_equal(a.head(), b.head())
  
  
  def test_no_overlaps():
      df = dd.demo.make_timeseries('2000', '2001', {'A': float},
                                   freq='3H', partition_freq='3M')
  
      assert all(df.get_division(i).index.max().compute()
               < df.get_division(i + 1).index.min().compute()
               for i in range(df.npartitions - 2))
  
subject: remove debug code