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