Python pandas.core.panel.Panel.from_dict() Examples
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Example #1
Source File: test_panel.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_from_dict_mixed_orient(self): df = tm.makeDataFrame() df['foo'] = 'bar' data = {'k1': df, 'k2': df} panel = Panel.from_dict(data, orient='minor') assert panel['foo'].values.dtype == np.object_ assert panel['A'].values.dtype == np.float64
Example #2
Source File: test_panel.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_shift(self): with catch_warnings(record=True): # major idx = self.panel.major_axis[0] idx_lag = self.panel.major_axis[1] shifted = self.panel.shift(1) assert_frame_equal(self.panel.major_xs(idx), shifted.major_xs(idx_lag)) # minor idx = self.panel.minor_axis[0] idx_lag = self.panel.minor_axis[1] shifted = self.panel.shift(1, axis='minor') assert_frame_equal(self.panel.minor_xs(idx), shifted.minor_xs(idx_lag)) # items idx = self.panel.items[0] idx_lag = self.panel.items[1] shifted = self.panel.shift(1, axis='items') assert_frame_equal(self.panel[idx], shifted[idx_lag]) # negative numbers, #2164 result = self.panel.shift(-1) expected = Panel({i: f.shift(-1)[:-1] for i, f in self.panel.iteritems()}) assert_panel_equal(result, expected) # mixed dtypes #6959 data = [('item ' + ch, makeMixedDataFrame()) for ch in list('abcde')] data = dict(data) mixed_panel = Panel.from_dict(data, orient='minor') shifted = mixed_panel.shift(1) assert_series_equal(mixed_panel.dtypes, shifted.dtypes)
Example #3
Source File: test_panel.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_from_dict_mixed_orient(self): with catch_warnings(record=True): df = tm.makeDataFrame() df['foo'] = 'bar' data = {'k1': df, 'k2': df} panel = Panel.from_dict(data, orient='minor') assert panel['foo'].values.dtype == np.object_ assert panel['A'].values.dtype == np.float64
Example #4
Source File: test_panel.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_ctor_orderedDict(self): with catch_warnings(record=True): keys = list(set(np.random.randint(0, 5000, 100)))[ :50] # unique random int keys d = OrderedDict([(k, mkdf(10, 5)) for k in keys]) p = Panel(d) assert list(p.items) == keys p = Panel.from_dict(d) assert list(p.items) == keys
Example #5
Source File: test_panel.py From coffeegrindsize with MIT License | 5 votes |
def test_shift(self): # major idx = self.panel.major_axis[0] idx_lag = self.panel.major_axis[1] shifted = self.panel.shift(1) assert_frame_equal(self.panel.major_xs(idx), shifted.major_xs(idx_lag)) # minor idx = self.panel.minor_axis[0] idx_lag = self.panel.minor_axis[1] shifted = self.panel.shift(1, axis='minor') assert_frame_equal(self.panel.minor_xs(idx), shifted.minor_xs(idx_lag)) # items idx = self.panel.items[0] idx_lag = self.panel.items[1] shifted = self.panel.shift(1, axis='items') assert_frame_equal(self.panel[idx], shifted[idx_lag]) # negative numbers, #2164 result = self.panel.shift(-1) expected = Panel({i: f.shift(-1)[:-1] for i, f in self.panel.iteritems()}) assert_panel_equal(result, expected) # mixed dtypes #6959 data = [('item ' + ch, makeMixedDataFrame()) for ch in list('abcde')] data = dict(data) mixed_panel = Panel.from_dict(data, orient='minor') shifted = mixed_panel.shift(1) assert_series_equal(mixed_panel.dtypes, shifted.dtypes)
Example #6
Source File: test_panel.py From coffeegrindsize with MIT License | 5 votes |
def test_from_dict_mixed_orient(self): df = tm.makeDataFrame() df['foo'] = 'bar' data = {'k1': df, 'k2': df} panel = Panel.from_dict(data, orient='minor') assert panel['foo'].values.dtype == np.object_ assert panel['A'].values.dtype == np.float64
Example #7
Source File: test_panel.py From coffeegrindsize with MIT License | 5 votes |
def test_ctor_orderedDict(self): keys = list(set(np.random.randint(0, 5000, 100)))[ :50] # unique random int keys d = OrderedDict([(k, mkdf(10, 5)) for k in keys]) p = Panel(d) assert list(p.items) == keys p = Panel.from_dict(d) assert list(p.items) == keys
Example #8
Source File: test_panel.py From elasticintel with GNU General Public License v3.0 | 5 votes |
def test_shift(self): with catch_warnings(record=True): # major idx = self.panel.major_axis[0] idx_lag = self.panel.major_axis[1] shifted = self.panel.shift(1) assert_frame_equal(self.panel.major_xs(idx), shifted.major_xs(idx_lag)) # minor idx = self.panel.minor_axis[0] idx_lag = self.panel.minor_axis[1] shifted = self.panel.shift(1, axis='minor') assert_frame_equal(self.panel.minor_xs(idx), shifted.minor_xs(idx_lag)) # items idx = self.panel.items[0] idx_lag = self.panel.items[1] shifted = self.panel.shift(1, axis='items') assert_frame_equal(self.panel[idx], shifted[idx_lag]) # negative numbers, #2164 result = self.panel.shift(-1) expected = Panel(dict((i, f.shift(-1)[:-1]) for i, f in self.panel.iteritems())) assert_panel_equal(result, expected) # mixed dtypes #6959 data = [('item ' + ch, makeMixedDataFrame()) for ch in list('abcde')] data = dict(data) mixed_panel = Panel.from_dict(data, orient='minor') shifted = mixed_panel.shift(1) assert_series_equal(mixed_panel.dtypes, shifted.dtypes)
Example #9
Source File: test_panel.py From elasticintel with GNU General Public License v3.0 | 5 votes |
def test_from_dict_mixed_orient(self): with catch_warnings(record=True): df = tm.makeDataFrame() df['foo'] = 'bar' data = {'k1': df, 'k2': df} panel = Panel.from_dict(data, orient='minor') assert panel['foo'].values.dtype == np.object_ assert panel['A'].values.dtype == np.float64
Example #10
Source File: test_panel.py From elasticintel with GNU General Public License v3.0 | 5 votes |
def test_ctor_orderedDict(self): with catch_warnings(record=True): keys = list(set(np.random.randint(0, 5000, 100)))[ :50] # unique random int keys d = OrderedDict([(k, mkdf(10, 5)) for k in keys]) p = Panel(d) assert list(p.items) == keys p = Panel.from_dict(d) assert list(p.items) == keys
Example #11
Source File: test_panel.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_shift(self): # major idx = self.panel.major_axis[0] idx_lag = self.panel.major_axis[1] shifted = self.panel.shift(1) assert_frame_equal(self.panel.major_xs(idx), shifted.major_xs(idx_lag)) # minor idx = self.panel.minor_axis[0] idx_lag = self.panel.minor_axis[1] shifted = self.panel.shift(1, axis='minor') assert_frame_equal(self.panel.minor_xs(idx), shifted.minor_xs(idx_lag)) # items idx = self.panel.items[0] idx_lag = self.panel.items[1] shifted = self.panel.shift(1, axis='items') assert_frame_equal(self.panel[idx], shifted[idx_lag]) # negative numbers, #2164 result = self.panel.shift(-1) expected = Panel({i: f.shift(-1)[:-1] for i, f in self.panel.iteritems()}) assert_panel_equal(result, expected) # mixed dtypes #6959 data = [('item ' + ch, makeMixedDataFrame()) for ch in list('abcde')] data = dict(data) mixed_panel = Panel.from_dict(data, orient='minor') shifted = mixed_panel.shift(1) assert_series_equal(mixed_panel.dtypes, shifted.dtypes)
Example #12
Source File: test_panel.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_ctor_orderedDict(self): keys = list(set(np.random.randint(0, 5000, 100)))[ :50] # unique random int keys d = OrderedDict([(k, mkdf(10, 5)) for k in keys]) p = Panel(d) assert list(p.items) == keys p = Panel.from_dict(d) assert list(p.items) == keys
Example #13
Source File: test_panel.py From recruit with Apache License 2.0 | 5 votes |
def test_ctor_orderedDict(self): keys = list(set(np.random.randint(0, 5000, 100)))[ :50] # unique random int keys d = OrderedDict([(k, mkdf(10, 5)) for k in keys]) p = Panel(d) assert list(p.items) == keys p = Panel.from_dict(d) assert list(p.items) == keys
Example #14
Source File: ols.py From Computable with MIT License | 5 votes |
def var_beta(self): """Returns the covariance of beta.""" result = {} result_index = self._result_index for i in range(len(self._var_beta_raw)): dm = DataFrame(self._var_beta_raw[i], columns=self.beta.columns, index=self.beta.columns) result[result_index[i]] = dm return Panel.from_dict(result, intersect=False)
Example #15
Source File: test_panel.py From recruit with Apache License 2.0 | 5 votes |
def test_from_dict_mixed_orient(self): df = tm.makeDataFrame() df['foo'] = 'bar' data = {'k1': df, 'k2': df} panel = Panel.from_dict(data, orient='minor') assert panel['foo'].values.dtype == np.object_ assert panel['A'].values.dtype == np.float64
Example #16
Source File: panel.py From Computable with MIT License | 5 votes |
def from_dict(cls, data): """ Analogous to Panel.from_dict """ return SparsePanel(data)
Example #17
Source File: test_panel.py From vnpy_crypto with MIT License | 5 votes |
def test_shift(self): with catch_warnings(record=True): # major idx = self.panel.major_axis[0] idx_lag = self.panel.major_axis[1] shifted = self.panel.shift(1) assert_frame_equal(self.panel.major_xs(idx), shifted.major_xs(idx_lag)) # minor idx = self.panel.minor_axis[0] idx_lag = self.panel.minor_axis[1] shifted = self.panel.shift(1, axis='minor') assert_frame_equal(self.panel.minor_xs(idx), shifted.minor_xs(idx_lag)) # items idx = self.panel.items[0] idx_lag = self.panel.items[1] shifted = self.panel.shift(1, axis='items') assert_frame_equal(self.panel[idx], shifted[idx_lag]) # negative numbers, #2164 result = self.panel.shift(-1) expected = Panel({i: f.shift(-1)[:-1] for i, f in self.panel.iteritems()}) assert_panel_equal(result, expected) # mixed dtypes #6959 data = [('item ' + ch, makeMixedDataFrame()) for ch in list('abcde')] data = dict(data) mixed_panel = Panel.from_dict(data, orient='minor') shifted = mixed_panel.shift(1) assert_series_equal(mixed_panel.dtypes, shifted.dtypes)
Example #18
Source File: test_panel.py From vnpy_crypto with MIT License | 5 votes |
def test_from_dict_mixed_orient(self): with catch_warnings(record=True): df = tm.makeDataFrame() df['foo'] = 'bar' data = {'k1': df, 'k2': df} panel = Panel.from_dict(data, orient='minor') assert panel['foo'].values.dtype == np.object_ assert panel['A'].values.dtype == np.float64
Example #19
Source File: test_panel.py From vnpy_crypto with MIT License | 5 votes |
def test_ctor_orderedDict(self): with catch_warnings(record=True): keys = list(set(np.random.randint(0, 5000, 100)))[ :50] # unique random int keys d = OrderedDict([(k, mkdf(10, 5)) for k in keys]) p = Panel(d) assert list(p.items) == keys p = Panel.from_dict(d) assert list(p.items) == keys
Example #20
Source File: test_panel.py From recruit with Apache License 2.0 | 5 votes |
def test_shift(self): # major idx = self.panel.major_axis[0] idx_lag = self.panel.major_axis[1] shifted = self.panel.shift(1) assert_frame_equal(self.panel.major_xs(idx), shifted.major_xs(idx_lag)) # minor idx = self.panel.minor_axis[0] idx_lag = self.panel.minor_axis[1] shifted = self.panel.shift(1, axis='minor') assert_frame_equal(self.panel.minor_xs(idx), shifted.minor_xs(idx_lag)) # items idx = self.panel.items[0] idx_lag = self.panel.items[1] shifted = self.panel.shift(1, axis='items') assert_frame_equal(self.panel[idx], shifted[idx_lag]) # negative numbers, #2164 result = self.panel.shift(-1) expected = Panel({i: f.shift(-1)[:-1] for i, f in self.panel.iteritems()}) assert_panel_equal(result, expected) # mixed dtypes #6959 data = [('item ' + ch, makeMixedDataFrame()) for ch in list('abcde')] data = dict(data) mixed_panel = Panel.from_dict(data, orient='minor') shifted = mixed_panel.shift(1) assert_series_equal(mixed_panel.dtypes, shifted.dtypes)
Example #21
Source File: test_panel.py From recruit with Apache License 2.0 | 4 votes |
def test_ctor_dict(self): itema = self.panel['ItemA'] itemb = self.panel['ItemB'] d = {'A': itema, 'B': itemb[5:]} d2 = {'A': itema._series, 'B': itemb[5:]._series} d3 = {'A': None, 'B': DataFrame(itemb[5:]._series), 'C': DataFrame(itema._series)} wp = Panel.from_dict(d) wp2 = Panel.from_dict(d2) # nested Dict # TODO: unused? wp3 = Panel.from_dict(d3) # noqa tm.assert_index_equal(wp.major_axis, self.panel.major_axis) assert_panel_equal(wp, wp2) # intersect wp = Panel.from_dict(d, intersect=True) tm.assert_index_equal(wp.major_axis, itemb.index[5:]) # use constructor assert_panel_equal(Panel(d), Panel.from_dict(d)) assert_panel_equal(Panel(d2), Panel.from_dict(d2)) assert_panel_equal(Panel(d3), Panel.from_dict(d3)) # a pathological case d4 = {'A': None, 'B': None} # TODO: unused? wp4 = Panel.from_dict(d4) # noqa assert_panel_equal(Panel(d4), Panel(items=['A', 'B'])) # cast dcasted = {k: v.reindex(wp.major_axis).fillna(0) for k, v in compat.iteritems(d)} result = Panel(dcasted, dtype=int) expected = Panel({k: v.astype(int) for k, v in compat.iteritems(dcasted)}) assert_panel_equal(result, expected) result = Panel(dcasted, dtype=np.int32) expected = Panel({k: v.astype(np.int32) for k, v in compat.iteritems(dcasted)}) assert_panel_equal(result, expected)
Example #22
Source File: plm.py From Computable with MIT License | 4 votes |
def _filter_data(self): """ """ data = self._x_orig cat_mapping = {} if isinstance(data, DataFrame): data = data.to_panel() else: if isinstance(data, Panel): data = data.copy() if not isinstance(data, SparsePanel): data, cat_mapping = self._convert_x(data) if not isinstance(data, Panel): data = Panel.from_dict(data, intersect=True) x_names = data.items if self._weights is not None: data['__weights__'] = self._weights # Filter x's without y (so we can make a prediction) filtered = data.to_frame() # Filter all data together using to_frame # convert to DataFrame y = self._y_orig if isinstance(y, Series): y = y.unstack() data['__y__'] = y data_long = data.to_frame() x_filt = filtered.filter(x_names) x = data_long.filter(x_names) y = data_long['__y__'] if self._weights is not None and not self._weights.empty: weights = data_long['__weights__'] else: weights = None return x, x_filt, y, weights, cat_mapping
Example #23
Source File: test_panel.py From twitter-stock-recommendation with MIT License | 4 votes |
def test_ctor_dict(self): with catch_warnings(record=True): itema = self.panel['ItemA'] itemb = self.panel['ItemB'] d = {'A': itema, 'B': itemb[5:]} d2 = {'A': itema._series, 'B': itemb[5:]._series} d3 = {'A': None, 'B': DataFrame(itemb[5:]._series), 'C': DataFrame(itema._series)} wp = Panel.from_dict(d) wp2 = Panel.from_dict(d2) # nested Dict # TODO: unused? wp3 = Panel.from_dict(d3) # noqa tm.assert_index_equal(wp.major_axis, self.panel.major_axis) assert_panel_equal(wp, wp2) # intersect wp = Panel.from_dict(d, intersect=True) tm.assert_index_equal(wp.major_axis, itemb.index[5:]) # use constructor assert_panel_equal(Panel(d), Panel.from_dict(d)) assert_panel_equal(Panel(d2), Panel.from_dict(d2)) assert_panel_equal(Panel(d3), Panel.from_dict(d3)) # a pathological case d4 = {'A': None, 'B': None} # TODO: unused? wp4 = Panel.from_dict(d4) # noqa assert_panel_equal(Panel(d4), Panel(items=['A', 'B'])) # cast dcasted = {k: v.reindex(wp.major_axis).fillna(0) for k, v in compat.iteritems(d)} result = Panel(dcasted, dtype=int) expected = Panel({k: v.astype(int) for k, v in compat.iteritems(dcasted)}) assert_panel_equal(result, expected) result = Panel(dcasted, dtype=np.int32) expected = Panel({k: v.astype(np.int32) for k, v in compat.iteritems(dcasted)}) assert_panel_equal(result, expected)
Example #24
Source File: test_panel.py From twitter-stock-recommendation with MIT License | 4 votes |
def test_constructor_dtypes(self): with catch_warnings(record=True): # GH #797 def _check_dtype(panel, dtype): for i in panel.items: assert panel[i].values.dtype.name == dtype # only nan holding types allowed here for dtype in ['float64', 'float32', 'object']: panel = Panel(items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel(np.array(np.random.randn(2, 10, 5), dtype=dtype), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel(np.array(np.random.randn(2, 10, 5), dtype='O'), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel( np.random.randn(2, 10, 5), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: df1 = DataFrame(np.random.randn(2, 5), index=lrange(2), columns=lrange(5)) df2 = DataFrame(np.random.randn(2, 5), index=lrange(2), columns=lrange(5)) panel = Panel.from_dict({'a': df1, 'b': df2}, dtype=dtype) _check_dtype(panel, dtype)
Example #25
Source File: test_panel.py From vnpy_crypto with MIT License | 4 votes |
def test_constructor_dtypes(self): with catch_warnings(record=True): # GH #797 def _check_dtype(panel, dtype): for i in panel.items: assert panel[i].values.dtype.name == dtype # only nan holding types allowed here for dtype in ['float64', 'float32', 'object']: panel = Panel(items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel(np.array(np.random.randn(2, 10, 5), dtype=dtype), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel(np.array(np.random.randn(2, 10, 5), dtype='O'), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel( np.random.randn(2, 10, 5), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: df1 = DataFrame(np.random.randn(2, 5), index=lrange(2), columns=lrange(5)) df2 = DataFrame(np.random.randn(2, 5), index=lrange(2), columns=lrange(5)) panel = Panel.from_dict({'a': df1, 'b': df2}, dtype=dtype) _check_dtype(panel, dtype)
Example #26
Source File: test_panel.py From vnpy_crypto with MIT License | 4 votes |
def test_ctor_dict(self): with catch_warnings(record=True): itema = self.panel['ItemA'] itemb = self.panel['ItemB'] d = {'A': itema, 'B': itemb[5:]} d2 = {'A': itema._series, 'B': itemb[5:]._series} d3 = {'A': None, 'B': DataFrame(itemb[5:]._series), 'C': DataFrame(itema._series)} wp = Panel.from_dict(d) wp2 = Panel.from_dict(d2) # nested Dict # TODO: unused? wp3 = Panel.from_dict(d3) # noqa tm.assert_index_equal(wp.major_axis, self.panel.major_axis) assert_panel_equal(wp, wp2) # intersect wp = Panel.from_dict(d, intersect=True) tm.assert_index_equal(wp.major_axis, itemb.index[5:]) # use constructor assert_panel_equal(Panel(d), Panel.from_dict(d)) assert_panel_equal(Panel(d2), Panel.from_dict(d2)) assert_panel_equal(Panel(d3), Panel.from_dict(d3)) # a pathological case d4 = {'A': None, 'B': None} # TODO: unused? wp4 = Panel.from_dict(d4) # noqa assert_panel_equal(Panel(d4), Panel(items=['A', 'B'])) # cast dcasted = {k: v.reindex(wp.major_axis).fillna(0) for k, v in compat.iteritems(d)} result = Panel(dcasted, dtype=int) expected = Panel({k: v.astype(int) for k, v in compat.iteritems(dcasted)}) assert_panel_equal(result, expected) result = Panel(dcasted, dtype=np.int32) expected = Panel({k: v.astype(np.int32) for k, v in compat.iteritems(dcasted)}) assert_panel_equal(result, expected)
Example #27
Source File: test_panel.py From coffeegrindsize with MIT License | 4 votes |
def test_ctor_dict(self): itema = self.panel['ItemA'] itemb = self.panel['ItemB'] d = {'A': itema, 'B': itemb[5:]} d2 = {'A': itema._series, 'B': itemb[5:]._series} d3 = {'A': None, 'B': DataFrame(itemb[5:]._series), 'C': DataFrame(itema._series)} wp = Panel.from_dict(d) wp2 = Panel.from_dict(d2) # nested Dict # TODO: unused? wp3 = Panel.from_dict(d3) # noqa tm.assert_index_equal(wp.major_axis, self.panel.major_axis) assert_panel_equal(wp, wp2) # intersect wp = Panel.from_dict(d, intersect=True) tm.assert_index_equal(wp.major_axis, itemb.index[5:]) # use constructor assert_panel_equal(Panel(d), Panel.from_dict(d)) assert_panel_equal(Panel(d2), Panel.from_dict(d2)) assert_panel_equal(Panel(d3), Panel.from_dict(d3)) # a pathological case d4 = {'A': None, 'B': None} # TODO: unused? wp4 = Panel.from_dict(d4) # noqa assert_panel_equal(Panel(d4), Panel(items=['A', 'B'])) # cast dcasted = {k: v.reindex(wp.major_axis).fillna(0) for k, v in compat.iteritems(d)} result = Panel(dcasted, dtype=int) expected = Panel({k: v.astype(int) for k, v in compat.iteritems(dcasted)}) assert_panel_equal(result, expected) result = Panel(dcasted, dtype=np.int32) expected = Panel({k: v.astype(np.int32) for k, v in compat.iteritems(dcasted)}) assert_panel_equal(result, expected)
Example #28
Source File: test_panel.py From coffeegrindsize with MIT License | 4 votes |
def test_constructor_dtypes(self): # GH #797 def _check_dtype(panel, dtype): for i in panel.items: assert panel[i].values.dtype.name == dtype # only nan holding types allowed here for dtype in ['float64', 'float32', 'object']: panel = Panel(items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel(np.array(np.random.randn(2, 10, 5), dtype=dtype), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel(np.array(np.random.randn(2, 10, 5), dtype='O'), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel( np.random.randn(2, 10, 5), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: df1 = DataFrame(np.random.randn(2, 5), index=lrange(2), columns=lrange(5)) df2 = DataFrame(np.random.randn(2, 5), index=lrange(2), columns=lrange(5)) panel = Panel.from_dict({'a': df1, 'b': df2}, dtype=dtype) _check_dtype(panel, dtype)
Example #29
Source File: test_panel.py From elasticintel with GNU General Public License v3.0 | 4 votes |
def test_ctor_dict(self): with catch_warnings(record=True): itema = self.panel['ItemA'] itemb = self.panel['ItemB'] d = {'A': itema, 'B': itemb[5:]} d2 = {'A': itema._series, 'B': itemb[5:]._series} d3 = {'A': None, 'B': DataFrame(itemb[5:]._series), 'C': DataFrame(itema._series)} wp = Panel.from_dict(d) wp2 = Panel.from_dict(d2) # nested Dict # TODO: unused? wp3 = Panel.from_dict(d3) # noqa tm.assert_index_equal(wp.major_axis, self.panel.major_axis) assert_panel_equal(wp, wp2) # intersect wp = Panel.from_dict(d, intersect=True) tm.assert_index_equal(wp.major_axis, itemb.index[5:]) # use constructor assert_panel_equal(Panel(d), Panel.from_dict(d)) assert_panel_equal(Panel(d2), Panel.from_dict(d2)) assert_panel_equal(Panel(d3), Panel.from_dict(d3)) # a pathological case d4 = {'A': None, 'B': None} # TODO: unused? wp4 = Panel.from_dict(d4) # noqa assert_panel_equal(Panel(d4), Panel(items=['A', 'B'])) # cast dcasted = dict((k, v.reindex(wp.major_axis).fillna(0)) for k, v in compat.iteritems(d)) result = Panel(dcasted, dtype=int) expected = Panel(dict((k, v.astype(int)) for k, v in compat.iteritems(dcasted))) assert_panel_equal(result, expected) result = Panel(dcasted, dtype=np.int32) expected = Panel(dict((k, v.astype(np.int32)) for k, v in compat.iteritems(dcasted))) assert_panel_equal(result, expected)
Example #30
Source File: test_panel.py From elasticintel with GNU General Public License v3.0 | 4 votes |
def test_constructor_dtypes(self): with catch_warnings(record=True): # GH #797 def _check_dtype(panel, dtype): for i in panel.items: assert panel[i].values.dtype.name == dtype # only nan holding types allowed here for dtype in ['float64', 'float32', 'object']: panel = Panel(items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel(np.array(np.random.randn(2, 10, 5), dtype=dtype), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel(np.array(np.random.randn(2, 10, 5), dtype='O'), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: panel = Panel( np.random.randn(2, 10, 5), items=lrange(2), major_axis=lrange(10), minor_axis=lrange(5), dtype=dtype) _check_dtype(panel, dtype) for dtype in ['float64', 'float32', 'int64', 'int32', 'object']: df1 = DataFrame(np.random.randn(2, 5), index=lrange(2), columns=lrange(5)) df2 = DataFrame(np.random.randn(2, 5), index=lrange(2), columns=lrange(5)) panel = Panel.from_dict({'a': df1, 'b': df2}, dtype=dtype) _check_dtype(panel, dtype)