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How to use dtypes method in pandera

Best Python code snippet using pandera_python

common.py

Source:common.py

1import numpy as np2from pandas import compat3from pandas.util._decorators import cache_readonly4import pandas.util.testing as tm5import pandas as pd6_seriesd = tm.getSeriesData()7_tsd = tm.getTimeSeriesData()8_frame = pd.DataFrame(_seriesd)9_frame2 = pd.DataFrame(_seriesd, columns=['D', 'C', 'B', 'A'])10_intframe = pd.DataFrame(dict((k, v.astype(int))11                              for k, v in compat.iteritems(_seriesd)))12_tsframe = pd.DataFrame(_tsd)13_mixed_frame = _frame.copy()14_mixed_frame['foo'] = 'bar'15class TestData(object):16    @cache_readonly17    def frame(self):18        return _frame.copy()19    @cache_readonly20    def frame2(self):21        return _frame2.copy()22    @cache_readonly23    def intframe(self):24        # force these all to int64 to avoid platform testing issues25        return pd.DataFrame(dict([(c, s) for c, s in26                                  compat.iteritems(_intframe)]),27                            dtype=np.int64)28    @cache_readonly29    def tsframe(self):30        return _tsframe.copy()31    @cache_readonly32    def mixed_frame(self):33        return _mixed_frame.copy()34    @cache_readonly35    def mixed_float(self):36        return pd.DataFrame({'A': _frame['A'].copy().astype('float32'),37                             'B': _frame['B'].copy().astype('float32'),38                             'C': _frame['C'].copy().astype('float16'),39                             'D': _frame['D'].copy().astype('float64')})40    @cache_readonly41    def mixed_float2(self):42        return pd.DataFrame({'A': _frame2['A'].copy().astype('float32'),43                             'B': _frame2['B'].copy().astype('float32'),44                             'C': _frame2['C'].copy().astype('float16'),45                             'D': _frame2['D'].copy().astype('float64')})46    @cache_readonly47    def mixed_int(self):48        return pd.DataFrame({'A': _intframe['A'].copy().astype('int32'),49                             'B': np.ones(len(_intframe['B']), dtype='uint64'),50                             'C': _intframe['C'].copy().astype('uint8'),51                             'D': _intframe['D'].copy().astype('int64')})52    @cache_readonly53    def all_mixed(self):54        return pd.DataFrame({'a': 1., 'b': 2, 'c': 'foo',55                             'float32': np.array([1.] * 10, dtype='float32'),56                             'int32': np.array([1] * 10, dtype='int32')},57                            index=np.arange(10))58    @cache_readonly59    def tzframe(self):60        result = pd.DataFrame({'A': pd.date_range('20130101', periods=3),61                               'B': pd.date_range('20130101', periods=3,62                                                  tz='US/Eastern'),63                               'C': pd.date_range('20130101', periods=3,64                                                  tz='CET')})65        result.iloc[1, 1] = pd.NaT66        result.iloc[1, 2] = pd.NaT67        return result68    @cache_readonly69    def empty(self):70        return pd.DataFrame({})71    @cache_readonly72    def ts1(self):73        return tm.makeTimeSeries(nper=30)74    @cache_readonly75    def ts2(self):76        return tm.makeTimeSeries(nper=30)[5:]77    @cache_readonly78    def simple(self):79        arr = np.array([[1., 2., 3.],80                        [4., 5., 6.],81                        [7., 8., 9.]])82        return pd.DataFrame(arr, columns=['one', 'two', 'three'],83                            index=['a', 'b', 'c'])84# self.ts3 = tm.makeTimeSeries()[-5:]85# self.ts4 = tm.makeTimeSeries()[1:-1]86def _check_mixed_float(df, dtype=None):87    # float16 are most likely to be upcasted to float3288    dtypes = dict(A='float32', B='float32', C='float16', D='float64')89    if isinstance(dtype, compat.string_types):90        dtypes = dict([(k, dtype) for k, v in dtypes.items()])91    elif isinstance(dtype, dict):92        dtypes.update(dtype)93    if dtypes.get('A'):94        assert(df.dtypes['A'] == dtypes['A'])95    if dtypes.get('B'):96        assert(df.dtypes['B'] == dtypes['B'])97    if dtypes.get('C'):98        assert(df.dtypes['C'] == dtypes['C'])99    if dtypes.get('D'):100        assert(df.dtypes['D'] == dtypes['D'])101def _check_mixed_int(df, dtype=None):102    dtypes = dict(A='int32', B='uint64', C='uint8', D='int64')103    if isinstance(dtype, compat.string_types):104        dtypes = dict([(k, dtype) for k, v in dtypes.items()])105    elif isinstance(dtype, dict):106        dtypes.update(dtype)107    if dtypes.get('A'):108        assert(df.dtypes['A'] == dtypes['A'])109    if dtypes.get('B'):110        assert(df.dtypes['B'] == dtypes['B'])111    if dtypes.get('C'):112        assert(df.dtypes['C'] == dtypes['C'])113    if dtypes.get('D'):...

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