/pandas/tests/frame/common.py
Python | 141 lines | 109 code | 28 blank | 4 comment | 16 complexity | cfa7353d40813e1fa541259556838d91 MD5 | raw file
Possible License(s): BSD-3-Clause, Apache-2.0
- 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'])