text: import numpy as np
  
  from pandas.util._decorators import cache_readonly
  
  import pandas as pd
  from pandas import compat
  import pandas.util.testing as tm
  
  _seriesd = tm.getSeriesData()
  _tsd = tm.getTimeSeriesData()
  
  _frame = pd.DataFrame(_seriesd)
  _frame2 = pd.DataFrame(_seriesd, columns=['D', 'C', 'B', 'A'])
  _intframe = pd.DataFrame({k: v.astype(int)
                            for k, v in compat.iteritems(_seriesd)})
  
  _tsframe = pd.DataFrame(_tsd)
  
  _mixed_frame = _frame.copy()
  _mixed_frame['foo'] = 'bar'
  
  
  class TestData(object):
  
      @cache_readonly
      def frame(self):
          return _frame.copy()
  
      @cache_readonly
      def frame2(self):
          return _frame2.copy()
  
      @cache_readonly
      def intframe(self):
          # force these all to int64 to avoid platform testing issues
          return pd.DataFrame({c: s for c, s in compat.iteritems(_intframe)},
                              dtype=np.int64)
  
      @cache_readonly
      def tsframe(self):
          return _tsframe.copy()
  
      @cache_readonly
      def mixed_frame(self):
          return _mixed_frame.copy()
  
      @cache_readonly
      def mixed_float(self):
          return pd.DataFrame({'A': _frame['A'].copy().astype('float32'),
                               'B': _frame['B'].copy().astype('float32'),
                               'C': _frame['C'].copy().astype('float16'),
                               'D': _frame['D'].copy().astype('float64')})
  
      @cache_readonly
      def mixed_float2(self):
          return pd.DataFrame({'A': _frame2['A'].copy().astype('float32'),
                               'B': _frame2['B'].copy().astype('float32'),
                               'C': _frame2['C'].copy().astype('float16'),
                               'D': _frame2['D'].copy().astype('float64')})
  
      @cache_readonly
      def mixed_int(self):
          return pd.DataFrame({'A': _intframe['A'].copy().astype('int32'),
                               'B': np.ones(len(_intframe['B']), dtype='uint64'),
                               'C': _intframe['C'].copy().astype('uint8'),
                               'D': _intframe['D'].copy().astype('int64')})
  
      @cache_readonly
      def all_mixed(self):
          return pd.DataFrame({'a': 1., 'b': 2, 'c': 'foo',
                               'float32': np.array([1.] * 10, dtype='float32'),
                               'int32': np.array([1] * 10, dtype='int32')},
                              index=np.arange(10))
  
      @cache_readonly
      def tzframe(self):
          result = pd.DataFrame({'A': pd.date_range('20130101', periods=3),
                                 'B': pd.date_range('20130101', periods=3,
                                                    tz='US/Eastern'),
                                 'C': pd.date_range('20130101', periods=3,
                                                    tz='CET')})
          result.iloc[1, 1] = pd.NaT
          result.iloc[1, 2] = pd.NaT
          return result
  
      @cache_readonly
      def empty(self):
          return pd.DataFrame()
  
      @cache_readonly
      def ts1(self):
          return tm.makeTimeSeries(nper=30)
  
      @cache_readonly
      def ts2(self):
          return tm.makeTimeSeries(nper=30)[5:]
  
      @cache_readonly
      def simple(self):
          arr = np.array([[1., 2., 3.],
                          [4., 5., 6.],
                          [7., 8., 9.]])
  
          return pd.DataFrame(arr, columns=['one', 'two', 'three'],
                              index=['a', 'b', 'c'])
  
  # self.ts3 = tm.makeTimeSeries()[-5:]
  # self.ts4 = tm.makeTimeSeries()[1:-1]
  
  
  def _check_mixed_float(df, dtype=None):
      # float16 are most likely to be upcasted to float32
      dtypes = dict(A='float32', B='float32', C='float16', D='float64')
      if isinstance(dtype, compat.string_types):
          dtypes = {k: dtype for k, v in dtypes.items()}
      elif isinstance(dtype, dict):
          dtypes.update(dtype)
      if dtypes.get('A'):
          assert(df.dtypes['A'] == dtypes['A'])
      if dtypes.get('B'):
          assert(df.dtypes['B'] == dtypes['B'])
      if dtypes.get('C'):
          assert(df.dtypes['C'] == dtypes['C'])
      if dtypes.get('D'):
          assert(df.dtypes['D'] == dtypes['D'])
  
  
  def _check_mixed_int(df, dtype=None):
      dtypes = dict(A='int32', B='uint64', C='uint8', D='int64')
      if isinstance(dtype, compat.string_types):
          dtypes = {k: dtype for k, v in dtypes.items()}
      elif isinstance(dtype, dict):
          dtypes.update(dtype)
      if dtypes.get('A'):
          assert(df.dtypes['A'] == dtypes['A'])
      if dtypes.get('B'):
          assert(df.dtypes['B'] == dtypes['B'])
      if dtypes.get('C'):
          assert(df.dtypes['C'] == dtypes['C'])
      if dtypes.get('D'):
          assert(df.dtypes['D'] == dtypes['D'])
  