/pandas/tests/test_panel.py
Python | 2558 lines | 1827 code | 557 blank | 174 comment | 96 complexity | 2ade495abdddf1bddcae962754764de4 MD5 | raw file
Possible License(s): BSD-3-Clause, Apache-2.0
- # -*- coding: utf-8 -*-
- # pylint: disable=W0612,E1101
- from datetime import datetime
- import operator
- import nose
- import numpy as np
- import pandas as pd
- from pandas.types.common import is_float_dtype
- from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
- from pandas.core.datetools import bday
- from pandas.core.nanops import nanall, nanany
- from pandas.core.panel import Panel
- from pandas.core.series import remove_na
- from pandas.formats.printing import pprint_thing
- from pandas import compat
- from pandas.compat import range, lrange, StringIO, OrderedDict, signature
- from pandas.util.testing import (assert_panel_equal, assert_frame_equal,
- assert_series_equal, assert_almost_equal,
- ensure_clean, assertRaisesRegexp,
- makeCustomDataframe as mkdf,
- makeMixedDataFrame)
- import pandas.core.panel as panelm
- import pandas.util.testing as tm
- class PanelTests(object):
- panel = None
- def test_pickle(self):
- unpickled = self.round_trip_pickle(self.panel)
- assert_frame_equal(unpickled['ItemA'], self.panel['ItemA'])
- def test_rank(self):
- self.assertRaises(NotImplementedError, lambda: self.panel.rank())
- def test_cumsum(self):
- cumsum = self.panel.cumsum()
- assert_frame_equal(cumsum['ItemA'], self.panel['ItemA'].cumsum())
- def not_hashable(self):
- c_empty = Panel()
- c = Panel(Panel([[[1]]]))
- self.assertRaises(TypeError, hash, c_empty)
- self.assertRaises(TypeError, hash, c)
- class SafeForLongAndSparse(object):
- _multiprocess_can_split_ = True
- def test_repr(self):
- repr(self.panel)
- def test_copy_names(self):
- for attr in ('major_axis', 'minor_axis'):
- getattr(self.panel, attr).name = None
- cp = self.panel.copy()
- getattr(cp, attr).name = 'foo'
- self.assertIsNone(getattr(self.panel, attr).name)
- def test_iter(self):
- tm.equalContents(list(self.panel), self.panel.items)
- def test_count(self):
- f = lambda s: notnull(s).sum()
- self._check_stat_op('count', f, obj=self.panel, has_skipna=False)
- def test_sum(self):
- self._check_stat_op('sum', np.sum)
- def test_mean(self):
- self._check_stat_op('mean', np.mean)
- def test_prod(self):
- self._check_stat_op('prod', np.prod)
- def test_median(self):
- def wrapper(x):
- if isnull(x).any():
- return np.nan
- return np.median(x)
- self._check_stat_op('median', wrapper)
- def test_min(self):
- self._check_stat_op('min', np.min)
- def test_max(self):
- self._check_stat_op('max', np.max)
- def test_skew(self):
- try:
- from scipy.stats import skew
- except ImportError:
- raise nose.SkipTest("no scipy.stats.skew")
- def this_skew(x):
- if len(x) < 3:
- return np.nan
- return skew(x, bias=False)
- self._check_stat_op('skew', this_skew)
- # def test_mad(self):
- # f = lambda x: np.abs(x - x.mean()).mean()
- # self._check_stat_op('mad', f)
- def test_var(self):
- def alt(x):
- if len(x) < 2:
- return np.nan
- return np.var(x, ddof=1)
- self._check_stat_op('var', alt)
- def test_std(self):
- def alt(x):
- if len(x) < 2:
- return np.nan
- return np.std(x, ddof=1)
- self._check_stat_op('std', alt)
- def test_sem(self):
- def alt(x):
- if len(x) < 2:
- return np.nan
- return np.std(x, ddof=1) / np.sqrt(len(x))
- self._check_stat_op('sem', alt)
- def _check_stat_op(self, name, alternative, obj=None, has_skipna=True):
- if obj is None:
- obj = self.panel
- # # set some NAs
- # obj.ix[5:10] = np.nan
- # obj.ix[15:20, -2:] = np.nan
- f = getattr(obj, name)
- if has_skipna:
- def skipna_wrapper(x):
- nona = remove_na(x)
- if len(nona) == 0:
- return np.nan
- return alternative(nona)
- def wrapper(x):
- return alternative(np.asarray(x))
- for i in range(obj.ndim):
- result = f(axis=i, skipna=False)
- assert_frame_equal(result, obj.apply(wrapper, axis=i))
- else:
- skipna_wrapper = alternative
- wrapper = alternative
- for i in range(obj.ndim):
- result = f(axis=i)
- if not tm._incompat_bottleneck_version(name):
- assert_frame_equal(result, obj.apply(skipna_wrapper, axis=i))
- self.assertRaises(Exception, f, axis=obj.ndim)
- # Unimplemented numeric_only parameter.
- if 'numeric_only' in signature(f).args:
- self.assertRaisesRegexp(NotImplementedError, name, f,
- numeric_only=True)
- class SafeForSparse(object):
- _multiprocess_can_split_ = True
- @classmethod
- def assert_panel_equal(cls, x, y):
- assert_panel_equal(x, y)
- def test_get_axis(self):
- assert (self.panel._get_axis(0) is self.panel.items)
- assert (self.panel._get_axis(1) is self.panel.major_axis)
- assert (self.panel._get_axis(2) is self.panel.minor_axis)
- def test_set_axis(self):
- new_items = Index(np.arange(len(self.panel.items)))
- new_major = Index(np.arange(len(self.panel.major_axis)))
- new_minor = Index(np.arange(len(self.panel.minor_axis)))
- # ensure propagate to potentially prior-cached items too
- item = self.panel['ItemA']
- self.panel.items = new_items
- if hasattr(self.panel, '_item_cache'):
- self.assertNotIn('ItemA', self.panel._item_cache)
- self.assertIs(self.panel.items, new_items)
- # TODO: unused?
- item = self.panel[0] # noqa
- self.panel.major_axis = new_major
- self.assertIs(self.panel[0].index, new_major)
- self.assertIs(self.panel.major_axis, new_major)
- # TODO: unused?
- item = self.panel[0] # noqa
- self.panel.minor_axis = new_minor
- self.assertIs(self.panel[0].columns, new_minor)
- self.assertIs(self.panel.minor_axis, new_minor)
- def test_get_axis_number(self):
- self.assertEqual(self.panel._get_axis_number('items'), 0)
- self.assertEqual(self.panel._get_axis_number('major'), 1)
- self.assertEqual(self.panel._get_axis_number('minor'), 2)
- with tm.assertRaisesRegexp(ValueError, "No axis named foo"):
- self.panel._get_axis_number('foo')
- with tm.assertRaisesRegexp(ValueError, "No axis named foo"):
- self.panel.__ge__(self.panel, axis='foo')
- def test_get_axis_name(self):
- self.assertEqual(self.panel._get_axis_name(0), 'items')
- self.assertEqual(self.panel._get_axis_name(1), 'major_axis')
- self.assertEqual(self.panel._get_axis_name(2), 'minor_axis')
- def test_get_plane_axes(self):
- # what to do here?
- index, columns = self.panel._get_plane_axes('items')
- index, columns = self.panel._get_plane_axes('major_axis')
- index, columns = self.panel._get_plane_axes('minor_axis')
- index, columns = self.panel._get_plane_axes(0)
- def test_truncate(self):
- dates = self.panel.major_axis
- start, end = dates[1], dates[5]
- trunced = self.panel.truncate(start, end, axis='major')
- expected = self.panel['ItemA'].truncate(start, end)
- assert_frame_equal(trunced['ItemA'], expected)
- trunced = self.panel.truncate(before=start, axis='major')
- expected = self.panel['ItemA'].truncate(before=start)
- assert_frame_equal(trunced['ItemA'], expected)
- trunced = self.panel.truncate(after=end, axis='major')
- expected = self.panel['ItemA'].truncate(after=end)
- assert_frame_equal(trunced['ItemA'], expected)
- # XXX test other axes
- def test_arith(self):
- self._test_op(self.panel, operator.add)
- self._test_op(self.panel, operator.sub)
- self._test_op(self.panel, operator.mul)
- self._test_op(self.panel, operator.truediv)
- self._test_op(self.panel, operator.floordiv)
- self._test_op(self.panel, operator.pow)
- self._test_op(self.panel, lambda x, y: y + x)
- self._test_op(self.panel, lambda x, y: y - x)
- self._test_op(self.panel, lambda x, y: y * x)
- self._test_op(self.panel, lambda x, y: y / x)
- self._test_op(self.panel, lambda x, y: y ** x)
- self._test_op(self.panel, lambda x, y: x + y) # panel + 1
- self._test_op(self.panel, lambda x, y: x - y) # panel - 1
- self._test_op(self.panel, lambda x, y: x * y) # panel * 1
- self._test_op(self.panel, lambda x, y: x / y) # panel / 1
- self._test_op(self.panel, lambda x, y: x ** y) # panel ** 1
- self.assertRaises(Exception, self.panel.__add__, self.panel['ItemA'])
- @staticmethod
- def _test_op(panel, op):
- result = op(panel, 1)
- assert_frame_equal(result['ItemA'], op(panel['ItemA'], 1))
- def test_keys(self):
- tm.equalContents(list(self.panel.keys()), self.panel.items)
- def test_iteritems(self):
- # Test panel.iteritems(), aka panel.iteritems()
- # just test that it works
- for k, v in self.panel.iteritems():
- pass
- self.assertEqual(len(list(self.panel.iteritems())),
- len(self.panel.items))
- def test_combineFrame(self):
- def check_op(op, name):
- # items
- df = self.panel['ItemA']
- func = getattr(self.panel, name)
- result = func(df, axis='items')
- assert_frame_equal(result['ItemB'], op(self.panel['ItemB'], df))
- # major
- xs = self.panel.major_xs(self.panel.major_axis[0])
- result = func(xs, axis='major')
- idx = self.panel.major_axis[1]
- assert_frame_equal(result.major_xs(idx),
- op(self.panel.major_xs(idx), xs))
- # minor
- xs = self.panel.minor_xs(self.panel.minor_axis[0])
- result = func(xs, axis='minor')
- idx = self.panel.minor_axis[1]
- assert_frame_equal(result.minor_xs(idx),
- op(self.panel.minor_xs(idx), xs))
- ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow', 'mod']
- if not compat.PY3:
- ops.append('div')
- for op in ops:
- try:
- check_op(getattr(operator, op), op)
- except:
- pprint_thing("Failing operation: %r" % op)
- raise
- if compat.PY3:
- try:
- check_op(operator.truediv, 'div')
- except:
- pprint_thing("Failing operation: %r" % 'div')
- raise
- def test_combinePanel(self):
- result = self.panel.add(self.panel)
- self.assert_panel_equal(result, self.panel * 2)
- def test_neg(self):
- self.assert_panel_equal(-self.panel, self.panel * -1)
- # issue 7692
- def test_raise_when_not_implemented(self):
- p = Panel(np.arange(3 * 4 * 5).reshape(3, 4, 5),
- items=['ItemA', 'ItemB', 'ItemC'],
- major_axis=pd.date_range('20130101', periods=4),
- minor_axis=list('ABCDE'))
- d = p.sum(axis=1).ix[0]
- ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'div', 'mod', 'pow']
- for op in ops:
- with self.assertRaises(NotImplementedError):
- getattr(p, op)(d, axis=0)
- def test_select(self):
- p = self.panel
- # select items
- result = p.select(lambda x: x in ('ItemA', 'ItemC'), axis='items')
- expected = p.reindex(items=['ItemA', 'ItemC'])
- self.assert_panel_equal(result, expected)
- # select major_axis
- result = p.select(lambda x: x >= datetime(2000, 1, 15), axis='major')
- new_major = p.major_axis[p.major_axis >= datetime(2000, 1, 15)]
- expected = p.reindex(major=new_major)
- self.assert_panel_equal(result, expected)
- # select minor_axis
- result = p.select(lambda x: x in ('D', 'A'), axis=2)
- expected = p.reindex(minor=['A', 'D'])
- self.assert_panel_equal(result, expected)
- # corner case, empty thing
- result = p.select(lambda x: x in ('foo', ), axis='items')
- self.assert_panel_equal(result, p.reindex(items=[]))
- def test_get_value(self):
- for item in self.panel.items:
- for mjr in self.panel.major_axis[::2]:
- for mnr in self.panel.minor_axis:
- result = self.panel.get_value(item, mjr, mnr)
- expected = self.panel[item][mnr][mjr]
- assert_almost_equal(result, expected)
- def test_abs(self):
- result = self.panel.abs()
- result2 = abs(self.panel)
- expected = np.abs(self.panel)
- self.assert_panel_equal(result, expected)
- self.assert_panel_equal(result2, expected)
- df = self.panel['ItemA']
- result = df.abs()
- result2 = abs(df)
- expected = np.abs(df)
- assert_frame_equal(result, expected)
- assert_frame_equal(result2, expected)
- s = df['A']
- result = s.abs()
- result2 = abs(s)
- expected = np.abs(s)
- assert_series_equal(result, expected)
- assert_series_equal(result2, expected)
- self.assertEqual(result.name, 'A')
- self.assertEqual(result2.name, 'A')
- class CheckIndexing(object):
- _multiprocess_can_split_ = True
- def test_getitem(self):
- self.assertRaises(Exception, self.panel.__getitem__, 'ItemQ')
- def test_delitem_and_pop(self):
- expected = self.panel['ItemA']
- result = self.panel.pop('ItemA')
- assert_frame_equal(expected, result)
- self.assertNotIn('ItemA', self.panel.items)
- del self.panel['ItemB']
- self.assertNotIn('ItemB', self.panel.items)
- self.assertRaises(Exception, self.panel.__delitem__, 'ItemB')
- values = np.empty((3, 3, 3))
- values[0] = 0
- values[1] = 1
- values[2] = 2
- panel = Panel(values, lrange(3), lrange(3), lrange(3))
- # did we delete the right row?
- panelc = panel.copy()
- del panelc[0]
- assert_frame_equal(panelc[1], panel[1])
- assert_frame_equal(panelc[2], panel[2])
- panelc = panel.copy()
- del panelc[1]
- assert_frame_equal(panelc[0], panel[0])
- assert_frame_equal(panelc[2], panel[2])
- panelc = panel.copy()
- del panelc[2]
- assert_frame_equal(panelc[1], panel[1])
- assert_frame_equal(panelc[0], panel[0])
- def test_setitem(self):
- # LongPanel with one item
- lp = self.panel.filter(['ItemA', 'ItemB']).to_frame()
- with tm.assertRaises(ValueError):
- self.panel['ItemE'] = lp
- # DataFrame
- df = self.panel['ItemA'][2:].filter(items=['A', 'B'])
- self.panel['ItemF'] = df
- self.panel['ItemE'] = df
- df2 = self.panel['ItemF']
- assert_frame_equal(df, df2.reindex(index=df.index, columns=df.columns))
- # scalar
- self.panel['ItemG'] = 1
- self.panel['ItemE'] = True
- self.assertEqual(self.panel['ItemG'].values.dtype, np.int64)
- self.assertEqual(self.panel['ItemE'].values.dtype, np.bool_)
- # object dtype
- self.panel['ItemQ'] = 'foo'
- self.assertEqual(self.panel['ItemQ'].values.dtype, np.object_)
- # boolean dtype
- self.panel['ItemP'] = self.panel['ItemA'] > 0
- self.assertEqual(self.panel['ItemP'].values.dtype, np.bool_)
- self.assertRaises(TypeError, self.panel.__setitem__, 'foo',
- self.panel.ix[['ItemP']])
- # bad shape
- p = Panel(np.random.randn(4, 3, 2))
- with tm.assertRaisesRegexp(ValueError,
- "shape of value must be \(3, 2\), "
- "shape of given object was \(4, 2\)"):
- p[0] = np.random.randn(4, 2)
- def test_setitem_ndarray(self):
- from pandas import date_range, datetools
- timeidx = date_range(start=datetime(2009, 1, 1),
- end=datetime(2009, 12, 31),
- freq=datetools.MonthEnd())
- lons_coarse = np.linspace(-177.5, 177.5, 72)
- lats_coarse = np.linspace(-87.5, 87.5, 36)
- P = Panel(items=timeidx, major_axis=lons_coarse,
- minor_axis=lats_coarse)
- data = np.random.randn(72 * 36).reshape((72, 36))
- key = datetime(2009, 2, 28)
- P[key] = data
- assert_almost_equal(P[key].values, data)
- def test_set_minor_major(self):
- # GH 11014
- df1 = DataFrame(['a', 'a', 'a', np.nan, 'a', np.nan])
- df2 = DataFrame([1.0, np.nan, 1.0, np.nan, 1.0, 1.0])
- panel = Panel({'Item1': df1, 'Item2': df2})
- newminor = notnull(panel.iloc[:, :, 0])
- panel.loc[:, :, 'NewMinor'] = newminor
- assert_frame_equal(panel.loc[:, :, 'NewMinor'],
- newminor.astype(object))
- newmajor = notnull(panel.iloc[:, 0, :])
- panel.loc[:, 'NewMajor', :] = newmajor
- assert_frame_equal(panel.loc[:, 'NewMajor', :],
- newmajor.astype(object))
- def test_major_xs(self):
- ref = self.panel['ItemA']
- idx = self.panel.major_axis[5]
- xs = self.panel.major_xs(idx)
- result = xs['ItemA']
- assert_series_equal(result, ref.xs(idx), check_names=False)
- self.assertEqual(result.name, 'ItemA')
- # not contained
- idx = self.panel.major_axis[0] - bday
- self.assertRaises(Exception, self.panel.major_xs, idx)
- def test_major_xs_mixed(self):
- self.panel['ItemD'] = 'foo'
- xs = self.panel.major_xs(self.panel.major_axis[0])
- self.assertEqual(xs['ItemA'].dtype, np.float64)
- self.assertEqual(xs['ItemD'].dtype, np.object_)
- def test_minor_xs(self):
- ref = self.panel['ItemA']
- idx = self.panel.minor_axis[1]
- xs = self.panel.minor_xs(idx)
- assert_series_equal(xs['ItemA'], ref[idx], check_names=False)
- # not contained
- self.assertRaises(Exception, self.panel.minor_xs, 'E')
- def test_minor_xs_mixed(self):
- self.panel['ItemD'] = 'foo'
- xs = self.panel.minor_xs('D')
- self.assertEqual(xs['ItemA'].dtype, np.float64)
- self.assertEqual(xs['ItemD'].dtype, np.object_)
- def test_xs(self):
- itemA = self.panel.xs('ItemA', axis=0)
- expected = self.panel['ItemA']
- assert_frame_equal(itemA, expected)
- # get a view by default
- itemA_view = self.panel.xs('ItemA', axis=0)
- itemA_view.values[:] = np.nan
- self.assertTrue(np.isnan(self.panel['ItemA'].values).all())
- # mixed-type yields a copy
- self.panel['strings'] = 'foo'
- result = self.panel.xs('D', axis=2)
- self.assertIsNotNone(result.is_copy)
- def test_getitem_fancy_labels(self):
- p = self.panel
- items = p.items[[1, 0]]
- dates = p.major_axis[::2]
- cols = ['D', 'C', 'F']
- # all 3 specified
- assert_panel_equal(p.ix[items, dates, cols],
- p.reindex(items=items, major=dates, minor=cols))
- # 2 specified
- assert_panel_equal(p.ix[:, dates, cols],
- p.reindex(major=dates, minor=cols))
- assert_panel_equal(p.ix[items, :, cols],
- p.reindex(items=items, minor=cols))
- assert_panel_equal(p.ix[items, dates, :],
- p.reindex(items=items, major=dates))
- # only 1
- assert_panel_equal(p.ix[items, :, :], p.reindex(items=items))
- assert_panel_equal(p.ix[:, dates, :], p.reindex(major=dates))
- assert_panel_equal(p.ix[:, :, cols], p.reindex(minor=cols))
- def test_getitem_fancy_slice(self):
- pass
- def test_getitem_fancy_ints(self):
- p = self.panel
- # #1603
- result = p.ix[:, -1, :]
- expected = p.ix[:, p.major_axis[-1], :]
- assert_frame_equal(result, expected)
- def test_getitem_fancy_xs(self):
- p = self.panel
- item = 'ItemB'
- date = p.major_axis[5]
- col = 'C'
- # get DataFrame
- # item
- assert_frame_equal(p.ix[item], p[item])
- assert_frame_equal(p.ix[item, :], p[item])
- assert_frame_equal(p.ix[item, :, :], p[item])
- # major axis, axis=1
- assert_frame_equal(p.ix[:, date], p.major_xs(date))
- assert_frame_equal(p.ix[:, date, :], p.major_xs(date))
- # minor axis, axis=2
- assert_frame_equal(p.ix[:, :, 'C'], p.minor_xs('C'))
- # get Series
- assert_series_equal(p.ix[item, date], p[item].ix[date])
- assert_series_equal(p.ix[item, date, :], p[item].ix[date])
- assert_series_equal(p.ix[item, :, col], p[item][col])
- assert_series_equal(p.ix[:, date, col], p.major_xs(date).ix[col])
- def test_getitem_fancy_xs_check_view(self):
- item = 'ItemB'
- date = self.panel.major_axis[5]
- # make sure it's always a view
- NS = slice(None, None)
- # DataFrames
- comp = assert_frame_equal
- self._check_view(item, comp)
- self._check_view((item, NS), comp)
- self._check_view((item, NS, NS), comp)
- self._check_view((NS, date), comp)
- self._check_view((NS, date, NS), comp)
- self._check_view((NS, NS, 'C'), comp)
- # Series
- comp = assert_series_equal
- self._check_view((item, date), comp)
- self._check_view((item, date, NS), comp)
- self._check_view((item, NS, 'C'), comp)
- self._check_view((NS, date, 'C'), comp)
- def test_getitem_callable(self):
- p = self.panel
- # GH 12533
- assert_frame_equal(p[lambda x: 'ItemB'], p.loc['ItemB'])
- assert_panel_equal(p[lambda x: ['ItemB', 'ItemC']],
- p.loc[['ItemB', 'ItemC']])
- def test_ix_setitem_slice_dataframe(self):
- a = Panel(items=[1, 2, 3], major_axis=[11, 22, 33],
- minor_axis=[111, 222, 333])
- b = DataFrame(np.random.randn(2, 3), index=[111, 333],
- columns=[1, 2, 3])
- a.ix[:, 22, [111, 333]] = b
- assert_frame_equal(a.ix[:, 22, [111, 333]], b)
- def test_ix_align(self):
- from pandas import Series
- b = Series(np.random.randn(10), name=0)
- b.sort()
- df_orig = Panel(np.random.randn(3, 10, 2))
- df = df_orig.copy()
- df.ix[0, :, 0] = b
- assert_series_equal(df.ix[0, :, 0].reindex(b.index), b)
- df = df_orig.swapaxes(0, 1)
- df.ix[:, 0, 0] = b
- assert_series_equal(df.ix[:, 0, 0].reindex(b.index), b)
- df = df_orig.swapaxes(1, 2)
- df.ix[0, 0, :] = b
- assert_series_equal(df.ix[0, 0, :].reindex(b.index), b)
- def test_ix_frame_align(self):
- p_orig = tm.makePanel()
- df = p_orig.ix[0].copy()
- assert_frame_equal(p_orig['ItemA'], df)
- p = p_orig.copy()
- p.ix[0, :, :] = df
- assert_panel_equal(p, p_orig)
- p = p_orig.copy()
- p.ix[0] = df
- assert_panel_equal(p, p_orig)
- p = p_orig.copy()
- p.iloc[0, :, :] = df
- assert_panel_equal(p, p_orig)
- p = p_orig.copy()
- p.iloc[0] = df
- assert_panel_equal(p, p_orig)
- p = p_orig.copy()
- p.loc['ItemA'] = df
- assert_panel_equal(p, p_orig)
- p = p_orig.copy()
- p.loc['ItemA', :, :] = df
- assert_panel_equal(p, p_orig)
- p = p_orig.copy()
- p['ItemA'] = df
- assert_panel_equal(p, p_orig)
- p = p_orig.copy()
- p.ix[0, [0, 1, 3, 5], -2:] = df
- out = p.ix[0, [0, 1, 3, 5], -2:]
- assert_frame_equal(out, df.iloc[[0, 1, 3, 5], [2, 3]])
- # GH3830, panel assignent by values/frame
- for dtype in ['float64', 'int64']:
- panel = Panel(np.arange(40).reshape((2, 4, 5)),
- items=['a1', 'a2'], dtype=dtype)
- df1 = panel.iloc[0]
- df2 = panel.iloc[1]
- tm.assert_frame_equal(panel.loc['a1'], df1)
- tm.assert_frame_equal(panel.loc['a2'], df2)
- # Assignment by Value Passes for 'a2'
- panel.loc['a2'] = df1.values
- tm.assert_frame_equal(panel.loc['a1'], df1)
- tm.assert_frame_equal(panel.loc['a2'], df1)
- # Assignment by DataFrame Ok w/o loc 'a2'
- panel['a2'] = df2
- tm.assert_frame_equal(panel.loc['a1'], df1)
- tm.assert_frame_equal(panel.loc['a2'], df2)
- # Assignment by DataFrame Fails for 'a2'
- panel.loc['a2'] = df2
- tm.assert_frame_equal(panel.loc['a1'], df1)
- tm.assert_frame_equal(panel.loc['a2'], df2)
- def _check_view(self, indexer, comp):
- cp = self.panel.copy()
- obj = cp.ix[indexer]
- obj.values[:] = 0
- self.assertTrue((obj.values == 0).all())
- comp(cp.ix[indexer].reindex_like(obj), obj)
- def test_logical_with_nas(self):
- d = Panel({'ItemA': {'a': [np.nan, False]},
- 'ItemB': {'a': [True, True]}})
- result = d['ItemA'] | d['ItemB']
- expected = DataFrame({'a': [np.nan, True]})
- assert_frame_equal(result, expected)
- # this is autodowncasted here
- result = d['ItemA'].fillna(False) | d['ItemB']
- expected = DataFrame({'a': [True, True]})
- assert_frame_equal(result, expected)
- def test_neg(self):
- # what to do?
- assert_panel_equal(-self.panel, -1 * self.panel)
- def test_invert(self):
- assert_panel_equal(-(self.panel < 0), ~(self.panel < 0))
- def test_comparisons(self):
- p1 = tm.makePanel()
- p2 = tm.makePanel()
- tp = p1.reindex(items=p1.items + ['foo'])
- df = p1[p1.items[0]]
- def test_comp(func):
- # versus same index
- result = func(p1, p2)
- self.assert_numpy_array_equal(result.values,
- func(p1.values, p2.values))
- # versus non-indexed same objs
- self.assertRaises(Exception, func, p1, tp)
- # versus different objs
- self.assertRaises(Exception, func, p1, df)
- # versus scalar
- result3 = func(self.panel, 0)
- self.assert_numpy_array_equal(result3.values,
- func(self.panel.values, 0))
- test_comp(operator.eq)
- test_comp(operator.ne)
- test_comp(operator.lt)
- test_comp(operator.gt)
- test_comp(operator.ge)
- test_comp(operator.le)
- def test_get_value(self):
- for item in self.panel.items:
- for mjr in self.panel.major_axis[::2]:
- for mnr in self.panel.minor_axis:
- result = self.panel.get_value(item, mjr, mnr)
- expected = self.panel[item][mnr][mjr]
- assert_almost_equal(result, expected)
- with tm.assertRaisesRegexp(TypeError,
- "There must be an argument for each axis"):
- self.panel.get_value('a')
- def test_set_value(self):
- for item in self.panel.items:
- for mjr in self.panel.major_axis[::2]:
- for mnr in self.panel.minor_axis:
- self.panel.set_value(item, mjr, mnr, 1.)
- assert_almost_equal(self.panel[item][mnr][mjr], 1.)
- # resize
- res = self.panel.set_value('ItemE', 'foo', 'bar', 1.5)
- tm.assertIsInstance(res, Panel)
- self.assertIsNot(res, self.panel)
- self.assertEqual(res.get_value('ItemE', 'foo', 'bar'), 1.5)
- res3 = self.panel.set_value('ItemE', 'foobar', 'baz', 5)
- self.assertTrue(is_float_dtype(res3['ItemE'].values))
- with tm.assertRaisesRegexp(TypeError,
- "There must be an argument for each axis"
- " plus the value provided"):
- self.panel.set_value('a')
- _panel = tm.makePanel()
- tm.add_nans(_panel)
- class TestPanel(tm.TestCase, PanelTests, CheckIndexing, SafeForLongAndSparse,
- SafeForSparse):
- _multiprocess_can_split_ = True
- @classmethod
- def assert_panel_equal(cls, x, y):
- assert_panel_equal(x, y)
- def setUp(self):
- self.panel = _panel.copy()
- self.panel.major_axis.name = None
- self.panel.minor_axis.name = None
- self.panel.items.name = None
- def test_panel_warnings(self):
- with tm.assert_produces_warning(FutureWarning):
- shifted1 = self.panel.shift(lags=1)
- with tm.assert_produces_warning(False):
- shifted2 = self.panel.shift(periods=1)
- tm.assert_panel_equal(shifted1, shifted2)
- with tm.assert_produces_warning(False):
- shifted3 = self.panel.shift()
- tm.assert_panel_equal(shifted1, shifted3)
- def test_constructor(self):
- # with BlockManager
- wp = Panel(self.panel._data)
- self.assertIs(wp._data, self.panel._data)
- wp = Panel(self.panel._data, copy=True)
- self.assertIsNot(wp._data, self.panel._data)
- assert_panel_equal(wp, self.panel)
- # strings handled prop
- wp = Panel([[['foo', 'foo', 'foo', ], ['foo', 'foo', 'foo']]])
- self.assertEqual(wp.values.dtype, np.object_)
- vals = self.panel.values
- # no copy
- wp = Panel(vals)
- self.assertIs(wp.values, vals)
- # copy
- wp = Panel(vals, copy=True)
- self.assertIsNot(wp.values, vals)
- # GH #8285, test when scalar data is used to construct a Panel
- # if dtype is not passed, it should be inferred
- value_and_dtype = [(1, 'int64'), (3.14, 'float64'),
- ('foo', np.object_)]
- for (val, dtype) in value_and_dtype:
- wp = Panel(val, items=range(2), major_axis=range(3),
- minor_axis=range(4))
- vals = np.empty((2, 3, 4), dtype=dtype)
- vals.fill(val)
- assert_panel_equal(wp, Panel(vals, dtype=dtype))
- # test the case when dtype is passed
- wp = Panel(1, items=range(2), major_axis=range(3), minor_axis=range(4),
- dtype='float32')
- vals = np.empty((2, 3, 4), dtype='float32')
- vals.fill(1)
- assert_panel_equal(wp, Panel(vals, dtype='float32'))
- def test_constructor_cast(self):
- zero_filled = self.panel.fillna(0)
- casted = Panel(zero_filled._data, dtype=int)
- casted2 = Panel(zero_filled.values, dtype=int)
- exp_values = zero_filled.values.astype(int)
- assert_almost_equal(casted.values, exp_values)
- assert_almost_equal(casted2.values, exp_values)
- casted = Panel(zero_filled._data, dtype=np.int32)
- casted2 = Panel(zero_filled.values, dtype=np.int32)
- exp_values = zero_filled.values.astype(np.int32)
- assert_almost_equal(casted.values, exp_values)
- assert_almost_equal(casted2.values, exp_values)
- # can't cast
- data = [[['foo', 'bar', 'baz']]]
- self.assertRaises(ValueError, Panel, data, dtype=float)
- def test_constructor_empty_panel(self):
- empty = Panel()
- self.assertEqual(len(empty.items), 0)
- self.assertEqual(len(empty.major_axis), 0)
- self.assertEqual(len(empty.minor_axis), 0)
- def test_constructor_observe_dtype(self):
- # GH #411
- panel = Panel(items=lrange(3), major_axis=lrange(3),
- minor_axis=lrange(3), dtype='O')
- self.assertEqual(panel.values.dtype, np.object_)
- def test_constructor_dtypes(self):
- # GH #797
- def _check_dtype(panel, dtype):
- for i in panel.items:
- self.assertEqual(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)
- def test_constructor_fails_with_not_3d_input(self):
- with tm.assertRaisesRegexp(ValueError,
- "The number of dimensions required is 3"):
- Panel(np.random.randn(10, 2))
- def test_consolidate(self):
- self.assertTrue(self.panel._data.is_consolidated())
- self.panel['foo'] = 1.
- self.assertFalse(self.panel._data.is_consolidated())
- panel = self.panel.consolidate()
- self.assertTrue(panel._data.is_consolidated())
- 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
- self.assert_index_equal(wp.major_axis, self.panel.major_axis)
- assert_panel_equal(wp, wp2)
- # intersect
- wp = Panel.from_dict(d, intersect=True)
- self.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)
- def test_constructor_dict_mixed(self):
- data = dict((k, v.values) for k, v in self.panel.iteritems())
- result = Panel(data)
- exp_major = Index(np.arange(len(self.panel.major_axis)))
- self.assert_index_equal(result.major_axis, exp_major)
- result = Panel(data, items=self.panel.items,
- major_axis=self.panel.major_axis,
- minor_axis=self.panel.minor_axis)
- assert_panel_equal(result, self.panel)
- data['ItemC'] = self.panel['ItemC']
- result = Panel(data)
- assert_panel_equal(result, self.panel)
- # corner, blow up
- data['ItemB'] = data['ItemB'][:-1]
- self.assertRaises(Exception, Panel, data)
- data['ItemB'] = self.panel['ItemB'].values[:, :-1]
- self.assertRaises(Exception, Panel, data)
- 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)
- self.assertTrue(list(p.items) == keys)
- p = Panel.from_dict(d)
- self.assertTrue(list(p.items) == keys)
- def test_constructor_resize(self):
- data = self.panel._data
- items = self.panel.items[:-1]
- major = self.panel.major_axis[:-1]
- minor = self.panel.minor_axis[:-1]
- result = Panel(data, items=items, major_axis=major, minor_axis=minor)
- expected = self.panel.reindex(items=items, major=major, minor=minor)
- assert_panel_equal(result, expected)
- result = Panel(data, items=items, major_axis=major)
- expected = self.panel.reindex(items=items, major=major)
- assert_panel_equal(result, expected)
- result = Panel(data, items=items)
- expected = self.panel.reindex(items=items)
- assert_panel_equal(result, expected)
- result = Panel(data, minor_axis=minor)
- expected = self.panel.reindex(minor=minor)
- assert_panel_equal(result, expected)
- 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')
- self.assertEqual(panel['foo'].values.dtype, np.object_)
- self.assertEqual(panel['A'].values.dtype, np.float64)
- def test_constructor_error_msgs(self):
- def testit():
- Panel(np.random.randn(3, 4, 5), lrange(4), lrange(5), lrange(5))
- assertRaisesRegexp(ValueError,
- "Shape of passed values is \(3, 4, 5\), "
- "indices imply \(4, 5, 5\)",
- testit)
- def testit():
- Panel(np.random.randn(3, 4, 5), lrange(5), lrange(4), lrange(5))
- assertRaisesRegexp(ValueError,
- "Shape of passed values is \(3, 4, 5\), "
- "indices imply \(5, 4, 5\)",
- testit)
- def testit():
- Panel(np.random.randn(3, 4, 5), lrange(5), lrange(5), lrange(4))
- assertRaisesRegexp(ValueError,
- "Shape of passed values is \(3, 4, 5\), "
- "indices imply \(5, 5, 4\)",
- testit)
- def test_conform(self):
- df = self.panel['ItemA'][:-5].filter(items=['A', 'B'])
- conformed = self.panel.conform(df)
- tm.assert_index_equal(conformed.index, self.panel.major_axis)
- tm.assert_index_equal(conformed.columns, self.panel.minor_axis)
- def test_convert_objects(self):
- # GH 4937
- p = Panel(dict(A=dict(a=['1', '1.0'])))
- expected = Panel(dict(A=dict(a=[1, 1.0])))
- result = p._convert(numeric=True, coerce=True)
- assert_panel_equal(result, expected)
- def test_dtypes(self):
- result = self.panel.dtypes
- expected = Series(np.dtype('float64'), index=self.panel.items)
- assert_series_equal(result, expected)
- def test_astype(self):
- # GH7271
- data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
- panel = Panel(data, ['a', 'b'], ['c', 'd'], ['e', 'f'])
- str_data = np.array([[['1', '2'], ['3', '4']],
- [['5', '6'], ['7', '8']]])
- expected = Panel(str_data, ['a', 'b'], ['c', 'd'], ['e', 'f'])
- assert_panel_equal(panel.astype(str), expected)
- self.assertRaises(NotImplementedError, panel.astype, {0: str})
- def test_apply(self):
- # GH1148
- # ufunc
- applied = self.panel.apply(np.sqrt)
- self.assertTrue(assert_almost_equal(applied.values, np.sqrt(
- self.panel.values)))
- # ufunc same shape
- result = self.panel.apply(lambda x: x * 2, axis='items')
- expected = self.panel * 2
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2, axis='major_axis')
- expected = self.panel * 2
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2, axis='minor_axis')
- expected = self.panel * 2
- assert_panel_equal(result, expected)
- # reduction to DataFrame
- result = self.panel.apply(lambda x: x.dtype, axis='items')
- expected = DataFrame(np.dtype('float64'), index=self.panel.major_axis,
- columns=self.panel.minor_axis)
- assert_frame_equal(result, expected)
- result = self.panel.apply(lambda x: x.dtype, axis='major_axis')
- expected = DataFrame(np.dtype('float64'), index=self.panel.minor_axis,
- columns=self.panel.items)
- assert_frame_equal(result, expected)
- result = self.panel.apply(lambda x: x.dtype, axis='minor_axis')
- expected = DataFrame(np.dtype('float64'), index=self.panel.major_axis,
- columns=self.panel.items)
- assert_frame_equal(result, expected)
- # reductions via other dims
- expected = self.panel.sum(0)
- result = self.panel.apply(lambda x: x.sum(), axis='items')
- assert_frame_equal(result, expected)
- expected = self.panel.sum(1)
- result = self.panel.apply(lambda x: x.sum(), axis='major_axis')
- assert_frame_equal(result, expected)
- expected = self.panel.sum(2)
- result = self.panel.apply(lambda x: x.sum(), axis='minor_axis')
- assert_frame_equal(result, expected)
- # pass kwargs
- result = self.panel.apply(lambda x, y: x.sum() + y, axis='items', y=5)
- expected = self.panel.sum(0) + 5
- assert_frame_equal(result, expected)
- def test_apply_slabs(self):
- # same shape as original
- result = self.panel.apply(lambda x: x * 2,
- axis=['items', 'major_axis'])
- expected = (self.panel * 2).transpose('minor_axis', 'major_axis',
- 'items')
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2,
- axis=['major_axis', 'items'])
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2,
- axis=['items', 'minor_axis'])
- expected = (self.panel * 2).transpose('major_axis', 'minor_axis',
- 'items')
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2,
- axis=['minor_axis', 'items'])
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2,
- axis=['major_axis', 'minor_axis'])
- expected = self.panel * 2
- assert_panel_equal(result, expected)
- result = self.panel.apply(lambda x: x * 2,
- axis=['minor_axis', 'major_axis'])
- assert_panel_equal(result, expected)
- # reductions
- result = self.panel.apply(lambda x: x.sum(0), axis=[
- 'items', 'major_axis'
- ])
- expected = self.panel.sum(1).T
- assert_frame_equal(result, expected)
- result = self.panel.apply(lambda x: x.sum(1), axis=[
- 'items', 'major_axis'
- ])
- expected = self.panel.sum(0)
- assert_frame_equal(result, expected)
- # transforms
- f = lambda x: ((x.T - x.mean(1)) / x.std(1)).T
- # make sure that we don't trigger any warnings
- with tm.assert_produces_warning(False):
- result = self.panel.apply(f, axis=['items', 'major_axis'])
- expected = Panel(dict([(ax, f(self.panel.loc[:, :, ax]))
- for ax in self.panel.minor_axis]))
- assert_panel_equal(result, expected)
- result = self.panel.apply(f, axis=['major_axis', 'minor_axis'])
- expected = Panel(dict([(ax, f(self.panel.loc[ax]))
- for ax in self.panel.items]))
- assert_panel_equal(result, expected)
- result = self.panel.apply(f, axis=['minor_axis', 'items'])
- expected = Panel(dict([(ax, f(self.panel.loc[:, ax]))
- for ax in self.panel.major_axis]))
- assert_panel_equal(result, expected)
- # with multi-indexes
- # GH7469
- index = MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), (
- 'two', 'a'), ('two', 'b')])
- dfa = DataFrame(np.array(np.arange(12, dtype='int64')).reshape(
- 4, 3), columns=list("ABC"), index=index)
- dfb = DataFrame(np.array(np.arange(10, 22, dtype='int64')).reshape(
- 4, 3), columns=list("ABC"), index=index)
- p = Panel({'f': dfa, 'g': dfb})
- result = p.apply(lambda x: x.sum(), axis=0)
- # on windows this will be in32
- result = result.astype('int64')
- expected = p.sum(0)
- assert_frame_equal(result, expected)
- def test_apply_no_or_zero_ndim(self):
- # GH10332
- self.panel = Panel(np.random.rand(5, 5, 5))
- result_int = self.panel.apply(lambda df: 0, axis=[1, 2])
- result_float = self.panel.apply(lambda df: 0.0, axis=[1, 2])
- result_int64 = self.panel.apply(lambda df: np.int64(0), axis=[1, 2])
- result_float64 = self.panel.apply(lambda df: np.float64(0.0),
- axis=[1, 2])
- expected_int = expected_int64 = Series([0] * 5)
- expected_float = expected_float64 = Series([0.0] * 5)
- assert_series_equal(result_int, expected_int)
- assert_series_equal(result_int64, expected_int64)
- assert_series_equal(result_float, expected_float)
- assert_series_equal(result_float64, expected_float64)
- def test_reindex(self):
- ref = self.panel['ItemB']
- # items
- result = self.panel.reindex(items=['ItemA', 'ItemB'])
- assert_frame_equal(result['ItemB'], ref)
- # major
- new_major = list(self.panel.major_axis[:10])
- result = self.panel.reindex(major=new_major)
- assert_frame_equal(result['ItemB'], ref.reindex(index=new_major))
- # raise exception put both major and major_axis
- self.assertRaises(Exception, self.panel.reindex, major_axis=new_major,
- major=new_major)
- # minor
- new_minor = list(self.panel.minor_axis[:2])
- result = self.panel.reindex(minor=new_minor)
- assert_frame_equal(result['ItemB'], ref.reindex(columns=new_minor))
- # this ok
- result = self.panel.reindex()
- assert_panel_equal(result, self.panel)
- self.assertFalse(result is self.panel)
- # with filling
- smaller_major = self.panel.major_axis[::5]
- smaller = self.panel.reindex(major=smaller_major)
- larger = smaller.reindex(major=self.panel.major_axis, method='pad')
- assert_frame_equal(larger.major_xs(self.panel.major_axis[1]),
- smaller.major_xs(smaller_major[0]))
- # don't necessarily copy
- result = self.panel.reindex(major=self.panel.major_axis, copy=False)
- assert_panel_equal(result, self.panel)
- self.assertTrue(result is self.panel)
- def test_reindex_multi(self):
- # with and without copy full reindexing
- result = self.panel.reindex(items=self.panel.items,
- major=self.panel.major_axis,
- minor=self.panel.minor_axis, copy=False)
- self.assertIs(result.items, self.panel.items)
- self.assertIs(result.major_axis, self.panel.major_axis)
- self.assertIs(result.minor_axis, self.panel.minor_axis)
- result = self.panel.reindex(items=self.panel.items,
- major=self.panel.major_axis,
- minor=self.panel.minor_axis, copy=False)
- assert_panel_equal(result, self.panel)
- # multi-axis indexing consistency
- # GH 5900
- df = DataFrame(np.random.randn(4, 3))
- p = Panel({'Item1': df})
- expected = Panel({'Item1': df})
- expected['Item2'] = np.nan
- items = ['Item1', 'Item2']
- major_axis = np.arange(4)
- minor_axis = np.arange(3)
- results = []
- results.append(p.reindex(items=items, major_axis=major_axis,
- copy=True))
- results.append(p.reindex(items=items, major_axis=major_axis,
- copy=False))
- results.append(p.reindex(items=items, minor_axis=minor_axis,
- copy=True))
- results.append(p.reindex(items=items, minor_axis=minor_axis,
- copy=False))
- results.append(p.reindex(items=items, major_axis=major_axis,
- minor_axis=minor_axis, copy=True))
- results.append(p.reindex(items=items, major_axis=major_axis,
- minor_axis=minor_axis, copy=False))
- for i, r in enumerate(results):
- assert_panel_equal(expected, r)
- def test_reindex_like(self):
- # reindex_like
- smaller = self.panel.reindex(items=self.panel.items[:-1],
- major=self.panel.major_axis[:-1],
- minor=self.panel.minor_axis[:-1])
- smaller_like = self.panel.reindex_like(smaller)
- assert_panel_equal(smaller, smaller_like)
- def test_take(self):
- # axis == 0
- result = self.panel.take([2, 0, 1], axis=0)
- expected = self.panel.reindex(items=['ItemC', 'ItemA', 'ItemB'])
- assert_panel_equal(result, expected)
- # axis >= 1
- result = self.panel.take([3, 0, 1, 2], axis=2)
- expected = self.panel.reindex(minor=['D', 'A', 'B', 'C'])
- assert_panel_equal(result, expected)
- # neg indicies ok
- expected = self.panel.reindex(minor=['D', 'D', 'B', 'C'])
- result = self.panel.take([3, -1, 1, 2], axis=2)
- assert_panel_equal(result, expected)
- self.assertRaises(Exception, self.panel.take, [4, 0, 1, 2], axis=2)
- def test_sort_index(self):
- import random
- ritems = list(self.panel.items)
- rmajor = list(self.panel.major_axis)
- rminor = list(self.panel.minor_axis)
- random.shuffle(ritems)
- random.shuffle(rmajor)
- random.shuffle(rminor)
- random_order = self.panel.reindex(items=ritems)
- sorted_panel = random_order.sort_index(axis=0)
- assert_panel_equal(sorted_panel, self.panel)
- # descending
- random_order = self.panel.reindex(items=ritems)
- sorted_panel = random_order.sort_index(axis=0, ascending=False)
- assert_panel_equal(sorted_panel,
- self.panel.reindex(items=self.panel.items[::-1]))
- random_order = self.panel.reindex(major=rmajor)
- sorted_panel = random_order.sort_index(axis=1)
- assert_panel_equal(sorted_panel, self.panel)
- random_order = self.panel.reindex(minor=rminor)
- sorted_panel = random_order.sort_index(axis=2)
- assert_panel_equal(sorted_panel, self.panel)
- def test_fillna(self):
- filled = self.panel.fillna(0)
- self.assertTrue(np.isfinite(filled.values).all())
- filled = self.panel.fillna(method='backfill')
- assert_frame_equal(filled['ItemA'],
- self.panel['ItemA'].fillna(method='backfill'))
- panel = self.panel.copy()
- panel['str'] = 'foo'
- filled = panel.fillna(method='backfill')
- assert_frame_equal(filled['ItemA'],
- panel['ItemA'].fillna(method='backfill'))
- empty = self.panel.reindex(items=[])
- filled = empty.fillna(0)
- assert_panel_equal(filled, empty)
- self.assertRaises(ValueError, self.panel.fillna)
- self.assertRaises(ValueError, self.panel.fillna, 5, method='ffill')
- self.assertRaises(TypeError, self.panel.fillna, [1, 2])
- self.assertRaises(TypeError, self.panel.fillna, (1, 2))
- # limit not implemented when only value is specified
- p = Panel(np.random.randn(3, 4, 5))
- p.iloc[0:2, 0:2, 0:2] = np.nan
- self.assertRaises(NotImplementedError, lambda: p.fillna(999, limit=1))
- # Test in place fillNA
- # Expected result
- expected = Panel([[[0, 1], [2, 1]], [[10, 11], [12, 11]]],
- items=['a', 'b'], minor_axis=['x', 'y'],
- dtype=np.float64)
- # method='ffill'
- p1 = Panel([[[0, 1], [2, np.nan]], [[10, 11], [12, np.nan]]],
- items=['a', 'b'], minor_axis=['x', 'y'],
- dtype=np.float64)
- p1.fillna(method='ffill', inplace=True)
- assert_panel_equal(p1, expected)
- # method='bfill'
- p2 = Panel([[[0, np.nan], [2, 1]], [[10, np.nan], [12, 11]]],
- items=['a', 'b'], minor_axis=['x', 'y'], dtype=np.float64)
- p2.fillna(method='bfill', inplace=True)
- assert_panel_equal(p2, expected)
- def test_ffill_bfill(self):
- assert_panel_equal(self.panel.ffill(),
- self.panel.fillna(method='ffill'))
- assert_panel_equal(self.panel.bfill(),
- self.panel.fillna(method='bfill'))
- def test_truncate_fillna_bug(self):
- # #1823
- result = self.panel.truncate(before=None, after=None, axis='items')
- # it works!
- result.fillna(value=0.0)
- def test_swapaxes(self):
- result = self.panel.swapaxes('items', 'minor')
- self.assertIs(result.items, self.panel.minor_axis)
- result = self.panel.swapaxes('items', 'major')
- self.assertIs(result.items, self.panel.major_axis)
- result = self.panel.swapaxes('major', 'minor')
- self.assertIs(result.major_axis, self.panel.minor_axis)
- panel = self.panel.copy()
- result = panel.swapaxes('major', 'minor')
- panel.values[0, 0, 1] = np.nan
- expected = panel.swapaxes('major', 'minor')
- assert_panel_equal(result, expected)
- # this should also work
- result = self.panel.swapaxes(0, 1)
- self.assertIs(result.items, self.panel.major_axis)
- # this works, but return a copy
- result = self.panel.swapaxes('items', 'items')
- assert_panel_equal(self.panel, result)
- self.assertNotEqual(id(self.panel), id(result))
- def test_transpose(self):
- result = self.panel.transpose('minor', 'major', 'items')
- expected = self.panel.swapaxes('items', 'minor')
- assert_panel_equal(result, expected)
- # test kwargs
- result = self.panel.transpose(items='minor', major='major',
- minor='items')
- expected = self.panel.swapaxes('items', 'minor')
- assert_panel_equal(result, expected)
- # text mixture of args
- result = self.panel.transpose('minor', major='major', minor='items')
- expected = self.panel.swapaxes('items', 'minor')
- assert_panel_equal(result, expected)
- result = self.panel.transpose('minor', 'major', minor='items')
- expected = self.panel.swapaxes('items', 'minor')
- assert_panel_equal(result, expected)
- # duplicate axes
- with tm.assertRaisesRegexp(TypeError,
- 'not enough/duplicate arguments'):
- self.panel.transpose('minor', maj='major', minor='items')
- with tm.assertRaisesRegexp(ValueError, 'repeated axis in transpose'):
- self.panel.transpose('minor', 'major', major='minor',
- minor='items')
- result = self.panel.transpose(2, 1, 0)
- assert_panel_equal(result, expected)
- result = self.panel.transpose('minor', 'items', 'major')
- expected = self.panel.swapaxes('items', 'minor')
- expected = expected.swapaxes('major', 'minor')
- assert_panel_equal(result, expected)
- result = self.panel.transpose(2, 0, 1)
- assert_panel_equal(result, expected)
- self.assertRaises(ValueError, self.panel.transpose, 0, 0, 1)
- def test_transpose_copy(self):
- panel = self.panel.copy()
- result = panel.transpose(2, 0, 1, copy=True)
- expected = panel.swapaxes('items', 'minor')
- expected = expected.swapaxes('major', 'minor')
- assert_panel_equal(result, expected)
- panel.values[0, 1, 1] = np.nan
- self.assertTrue(notnull(result.values[1, 0, 1]))
- def test_to_frame(self):
- # filtered
- filtered = self.panel.to_frame()
- expected = self.panel.to_frame().dropna(how='any')
- assert_frame_equal(filtered, expected)
- # unfiltered
- unfiltered = self.panel.to_frame(filter_observations=False)
- assert_panel_equal(unfiltered.to_panel(), self.panel)
- # names
- self.assertEqual(unfiltered.index.names, ('major', 'minor'))
- # unsorted, round trip
- df = self.panel.to_frame(filter_observations=False)
- unsorted = df.take(np.random.permutation(len(df)))
- pan = unsorted.to_panel()
- assert_panel_equal(pan, self.panel)
- # preserve original index names
- df = DataFrame(np.random.randn(6, 2),
- index=[['a', 'a', 'b', 'b', 'c', 'c'],
- [0, 1, 0, 1, 0, 1]],
- columns=['one', 'two'])
- df.index.names = ['foo', 'bar']
- df.columns.name = 'baz'
- rdf = df.to_panel().to_frame()
- self.assertEqual(rdf.index.names, df.index.names)
- self.assertEqual(rdf.columns.names, df.columns.names)
- def test_to_frame_mixed(self):
- panel = self.panel.fillna(0)
- panel['str'] = 'foo'
- panel['bool'] = panel['ItemA'] > 0
- lp = panel.to_frame()
- wp = lp.to_panel()
- self.assertEqual(wp['bool'].values.dtype, np.bool_)
- # Previously, this was mutating the underlying index and changing its
- # name
- assert_frame_equal(wp['bool'], panel['bool'], check_names=False)
- # GH 8704
- # with categorical
- df = panel.to_frame()
- df['category'] = df['str'].astype('category')
- # to_panel
- # TODO: this converts back to object
- p = df.to_panel()
- expected = panel.copy()
- expected['category'] = 'foo'
- assert_panel_equal(p, expected)
- def test_to_frame_multi_major(self):
- idx = MultiIndex.from_tuples([(1, 'one'), (1, 'two'), (2, 'one'), (
- 2, 'two')])
- df = DataFrame([[1, 'a', 1], [2, 'b', 1], [3, 'c', 1], [4, 'd', 1]],
- columns=['A', 'B', 'C'], index=idx)
- wp = Panel({'i1': df, 'i2': df})
- expected_idx = MultiIndex.from_tuples(
- [
- (1, 'one', 'A'), (1, 'one', 'B'),
- (1, 'one', 'C'), (1, 'two', 'A'),
- (1, 'two', 'B'), (1, 'two', 'C'),
- (2, 'one', 'A'), (2, 'one', 'B'),
- (2, 'one', 'C'), (2, 'two', 'A'),
- (2, 'two', 'B'), (2, 'two', 'C')
- ],
- names=[None, None, 'minor'])
- expected = DataFrame({'i1': [1, 'a', 1, 2, 'b', 1, 3,
- 'c', 1, 4, 'd', 1],
- 'i2': [1, 'a', 1, 2, 'b',
- 1, 3, 'c', 1, 4, 'd', 1]},
- index=expected_idx)
- result = wp.to_frame()
- assert_frame_equal(result, expected)
- wp.iloc[0, 0].iloc[0] = np.nan # BUG on setting. GH #5773
- result = wp.to_frame()
- assert_frame_equal(result, expected[1:])
- idx = MultiIndex.from_tuples([(1, 'two'), (1, 'one'), (2, 'one'), (
- np.nan, 'two')])
- df = DataFrame([[1, 'a', 1], [2, 'b', 1], [3, 'c', 1], [4, 'd', 1]],
- columns=['A', 'B', 'C'], index=idx)
- wp = Panel({'i1': df, 'i2': df})
- ex_idx = MultiIndex.from_tuples([(1, 'two', 'A'), (1, 'two', 'B'),
- (1, 'two', 'C'),
- (1, 'one', 'A'),
- (1, 'one', 'B'),
- (1, 'one', 'C'),
- (2, 'one', 'A'),
- (2, 'one', 'B'),
- (2, 'one', 'C'),
- (np.nan, 'two', 'A'),
- (np.nan, 'two', 'B'),
- (np.nan, 'two', 'C')],
- names=[None, None, 'minor'])
- expected.index = ex_idx
- result = wp.to_frame()
- assert_frame_equal(result, expected)
- def test_to_frame_multi_major_minor(self):
- cols = MultiIndex(levels=[['C_A', 'C_B'], ['C_1', 'C_2']],
- labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
- idx = MultiIndex.from_tuples([(1, 'one'), (1, 'two'), (2, 'one'), (
- 2, 'two'), (3, 'three'), (4, 'four')])
- df = DataFrame([[1, 2, 11, 12], [3, 4, 13, 14],
- ['a', 'b', 'w', 'x'],
- ['c', 'd', 'y', 'z'], [-1, -2, -3, -4],
- [-5, -6, -7, -8]], columns=cols, index=idx)
- wp = Panel({'i1': df, 'i2': df})
- exp_idx = MultiIndex.from_tuples(
- [(1, 'one', 'C_A', 'C_1'), (1, 'one', 'C_A', 'C_2'),
- (1, 'one', 'C_B', 'C_1'), (1, 'one', 'C_B', 'C_2'),
- (1, 'two', 'C_A', 'C_1'), (1, 'two', 'C_A', 'C_2'),
- (1, 'two', 'C_B', 'C_1'), (1, 'two', 'C_B', 'C_2'),
- (2, 'one', 'C_A', 'C_1'), (2, 'one', 'C_A', 'C_2'),
- (2, 'one', 'C_B', 'C_1'), (2, 'one', 'C_B', 'C_2'),
- (2, 'two', 'C_A', 'C_1'), (2, 'two', 'C_A', 'C_2'),
- (2, 'two', 'C_B', 'C_1'), (2, 'two', 'C_B', 'C_2'),
- (3, 'three', 'C_A', 'C_1'), (3, 'three', 'C_A', 'C_2'),
- (3, 'three', 'C_B', 'C_1'), (3, 'three', 'C_B', 'C_2'),
- (4, 'four', 'C_A', 'C_1'), (4, 'four', 'C_A', 'C_2'),
- (4, 'four', 'C_B', 'C_1'), (4, 'four', 'C_B', 'C_2')],
- names=[None, None, None, None])
- exp_val = [[1, 1], [2, 2], [11, 11], [12, 12], [3, 3], [4, 4],
- [13, 13], [14, 14], ['a', 'a'], ['b', 'b'], ['w', 'w'],
- ['x', 'x'], ['c', 'c'], ['d', 'd'], ['y', 'y'], ['z', 'z'],
- [-1, -1], [-2, -2], [-3, -3], [-4, -4], [-5, -5], [-6, -6],
- [-7, -7], [-8, -8]]
- result = wp.to_frame()
- expected = DataFrame(exp_val, columns=['i1', 'i2'], index=exp_idx)
- assert_frame_equal(result, expected)
- def test_to_frame_multi_drop_level(self):
- idx = MultiIndex.from_tuples([(1, 'one'), (2, 'one'), (2, 'two')])
- df = DataFrame({'A': [np.nan, 1, 2]}, index=idx)
- wp = Panel({'i1': df, 'i2': df})
- result = wp.to_frame()
- exp_idx = MultiIndex.from_tuples([(2, 'one', 'A'), (2, 'two', 'A')],
- names=[None, None, 'minor'])
- expected = DataFrame({'i1': [1., 2], 'i2': [1., 2]}, index=exp_idx)
- assert_frame_equal(result, expected)
- def test_to_panel_na_handling(self):
- df = DataFrame(np.random.randint(0, 10, size=20).reshape((10, 2)),
- index=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
- [0, 1, 2, 3, 4, 5, 2, 3, 4, 5]])
- panel = df.to_panel()
- self.assertTrue(isnull(panel[0].ix[1, [0, 1]]).all())
- def test_to_panel_duplicates(self):
- # #2441
- df = DataFrame({'a': [0, 0, 1], 'b': [1, 1, 1], 'c': [1, 2, 3]})
- idf = df.set_index(['a', 'b'])
- assertRaisesRegexp(ValueError, 'non-uniquely indexed', idf.to_panel)
- def test_panel_dups(self):
- # GH 4960
- # duplicates in an index
- # items
- data = np.random.randn(5, 100, 5)
- no_dup_panel = Panel(data, items=list("ABCDE"))
- panel = Panel(data, items=list("AACDE"))
- expected = no_dup_panel['A']
- result = panel.iloc[0]
- assert_frame_equal(result, expected)
- expected = no_dup_panel['E']
- result = panel.loc['E']
- assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[['A', 'B']]
- expected.items = ['A', 'A']
- result = panel.loc['A']
- assert_panel_equal(result, expected)
- # major
- data = np.random.randn(5, 5, 5)
- no_dup_panel = Panel(data, major_axis=list("ABCDE"))
- panel = Panel(data, major_axis=list("AACDE"))
- expected = no_dup_panel.loc[:, 'A']
- result = panel.iloc[:, 0]
- assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[:, 'E']
- result = panel.loc[:, 'E']
- assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[:, ['A', 'B']]
- expected.major_axis = ['A', 'A']
- result = panel.loc[:, 'A']
- assert_panel_equal(result, expected)
- # minor
- data = np.random.randn(5, 100, 5)
- no_dup_panel = Panel(data, minor_axis=list("ABCDE"))
- panel = Panel(data, minor_axis=list("AACDE"))
- expected = no_dup_panel.loc[:, :, 'A']
- result = panel.iloc[:, :, 0]
- assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[:, :, 'E']
- result = panel.loc[:, :, 'E']
- assert_frame_equal(result, expected)
- expected = no_dup_panel.loc[:, :, ['A', 'B']]
- expected.minor_axis = ['A', 'A']
- result = panel.loc[:, :, 'A']
- assert_panel_equal(result, expected)
- def test_filter(self):
- pass
- def test_compound(self):
- compounded = self.panel.compound()
- assert_series_equal(compounded['ItemA'],
- (1 + self.panel['ItemA']).product(0) - 1,
- check_names=False)
- 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(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)
- def test_tshift(self):
- # PeriodIndex
- ps = tm.makePeriodPanel()
- shifted = ps.tshift(1)
- unshifted = shifted.tshift(-1)
- assert_panel_equal(unshifted, ps)
- shifted2 = ps.tshift(freq='B')
- assert_panel_equal(shifted, shifted2)
- shifted3 = ps.tshift(freq=bday)
- assert_panel_equal(shifted, shifted3)
- assertRaisesRegexp(ValueError, 'does not match', ps.tshift, freq='M')
- # DatetimeIndex
- panel = _panel
- shifted = panel.tshift(1)
- unshifted = shifted.tshift(-1)
- assert_panel_equal(panel, unshifted)
- shifted2 = panel.tshift(freq=panel.major_axis.freq)
- assert_panel_equal(shifted, shifted2)
- inferred_ts = Panel(panel.values, items=panel.items,
- major_axis=Index(np.asarray(panel.major_axis)),
- minor_axis=panel.minor_axis)
- shifted = inferred_ts.tshift(1)
- unshifted = shifted.tshift(-1)
- assert_panel_equal(shifted, panel.tshift(1))
- assert_panel_equal(unshifted, inferred_ts)
- no_freq = panel.ix[:, [0, 5, 7], :]
- self.assertRaises(ValueError, no_freq.tshift)
- def test_pct_change(self):
- df1 = DataFrame({'c1': [1, 2, 5], 'c2': [3, 4, 6]})
- df2 = df1 + 1
- df3 = DataFrame({'c1': [3, 4, 7], 'c2': [5, 6, 8]})
- wp = Panel({'i1': df1, 'i2': df2, 'i3': df3})
- # major, 1
- result = wp.pct_change() # axis='major'
- expected = Panel({'i1': df1.pct_change(),
- 'i2': df2.pct_change(),
- 'i3': df3.pct_change()})
- assert_panel_equal(result, expected)
- result = wp.pct_change(axis=1)
- assert_panel_equal(result, expected)
- # major, 2
- result = wp.pct_change(periods=2)
- expected = Panel({'i1': df1.pct_change(2),
- 'i2': df2.pct_change(2),
- 'i3': df3.pct_change(2)})
- assert_panel_equal(result, expected)
- # minor, 1
- result = wp.pct_change(axis='minor')
- expected = Panel({'i1': df1.pct_change(axis=1),
- 'i2': df2.pct_change(axis=1),
- 'i3': df3.pct_change(axis=1)})
- assert_panel_equal(result, expected)
- result = wp.pct_change(axis=2)
- assert_panel_equal(result, expected)
- # minor, 2
- result = wp.pct_change(periods=2, axis='minor')
- expected = Panel({'i1': df1.pct_change(periods=2, axis=1),
- 'i2': df2.pct_change(periods=2, axis=1),
- 'i3': df3.pct_change(periods=2, axis=1)})
- assert_panel_equal(result, expected)
- # items, 1
- result = wp.pct_change(axis='items')
- expected = Panel({'i1': DataFrame({'c1': [np.nan, np.nan, np.nan],
- 'c2': [np.nan, np.nan, np.nan]}),
- 'i2': DataFrame({'c1': [1, 0.5, .2],
- 'c2': [1. / 3, 0.25, 1. / 6]}),
- 'i3': DataFrame({'c1': [.5, 1. / 3, 1. / 6],
- 'c2': [.25, .2, 1. / 7]})})
- assert_panel_equal(result, expected)
- result = wp.pct_change(axis=0)
- assert_panel_equal(result, expected)
- # items, 2
- result = wp.pct_change(periods=2, axis='items')
- expected = Panel({'i1': DataFrame({'c1': [np.nan, np.nan, np.nan],
- 'c2': [np.nan, np.nan, np.nan]}),
- 'i2': DataFrame({'c1': [np.nan, np.nan, np.nan],
- 'c2': [np.nan, np.nan, np.nan]}),
- 'i3': DataFrame({'c1': [2, 1, .4],
- 'c2': [2. / 3, .5, 1. / 3]})})
- assert_panel_equal(result, expected)
- def test_round(self):
- values = [[[-3.2, 2.2], [0, -4.8213], [3.123, 123.12],
- [-1566.213, 88.88], [-12, 94.5]],
- [[-5.82, 3.5], [6.21, -73.272], [-9.087, 23.12],
- [272.212, -99.99], [23, -76.5]]]
- evalues = [[[float(np.around(i)) for i in j] for j in k]
- for k in values]
- p = Panel(values, items=['Item1', 'Item2'],
- major_axis=pd.date_range('1/1/2000', periods=5),
- minor_axis=['A', 'B'])
- expected = Panel(evalues, items=['Item1', 'Item2'],
- major_axis=pd.date_range('1/1/2000', periods=5),
- minor_axis=['A', 'B'])
- result = p.round()
- self.assert_panel_equal(expected, result)
- def test_numpy_round(self):
- values = [[[-3.2, 2.2], [0, -4.8213], [3.123, 123.12],
- [-1566.213, 88.88], [-12, 94.5]],
- [[-5.82, 3.5], [6.21, -73.272], [-9.087, 23.12],
- [272.212, -99.99], [23, -76.5]]]
- evalues = [[[float(np.around(i)) for i in j] for j in k]
- for k in values]
- p = Panel(values, items=['Item1', 'Item2'],
- major_axis=pd.date_range('1/1/2000', periods=5),
- minor_axis=['A', 'B'])
- expected = Panel(evalues, items=['Item1', 'Item2'],
- major_axis=pd.date_range('1/1/2000', periods=5),
- minor_axis=['A', 'B'])
- result = np.round(p)
- self.assert_panel_equal(expected, result)
- msg = "the 'out' parameter is not supported"
- tm.assertRaisesRegexp(ValueError, msg, np.round, p, out=p)
- def test_multiindex_get(self):
- ind = MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)],
- names=['first', 'second'])
- wp = Panel(np.random.random((4, 5, 5)),
- items=ind,
- major_axis=np.arange(5),
- minor_axis=np.arange(5))
- f1 = wp['a']
- f2 = wp.ix['a']
- assert_panel_equal(f1, f2)
- self.assertTrue((f1.items == [1, 2]).all())
- self.assertTrue((f2.items == [1, 2]).all())
- ind = MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)],
- names=['first', 'second'])
- def test_multiindex_blocks(self):
- ind = MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)],
- names=['first', 'second'])
- wp = Panel(self.panel._data)
- wp.items = ind
- f1 = wp['a']
- self.assertTrue((f1.items == [1, 2]).all())
- f1 = wp[('b', 1)]
- self.assertTrue((f1.columns == ['A', 'B', 'C', 'D']).all())
- def test_repr_empty(self):
- empty = Panel()
- repr(empty)
- def test_rename(self):
- mapper = {'ItemA': 'foo', 'ItemB': 'bar', 'ItemC': 'baz'}
- renamed = self.panel.rename_axis(mapper, axis=0)
- exp = Index(['foo', 'bar', 'baz'])
- self.assert_index_equal(renamed.items, exp)
- renamed = self.panel.rename_axis(str.lower, axis=2)
- exp = Index(['a', 'b', 'c', 'd'])
- self.assert_index_equal(renamed.minor_axis, exp)
- # don't copy
- renamed_nocopy = self.panel.rename_axis(mapper, axis=0, copy=False)
- renamed_nocopy['foo'] = 3.
- self.assertTrue((self.panel['ItemA'].values == 3).all())
- def test_get_attr(self):
- assert_frame_equal(self.panel['ItemA'], self.panel.ItemA)
- # specific cases from #3440
- self.panel['a'] = self.panel['ItemA']
- assert_frame_equal(self.panel['a'], self.panel.a)
- self.panel['i'] = self.panel['ItemA']
- assert_frame_equal(self.panel['i'], self.panel.i)
- def test_from_frame_level1_unsorted(self):
- tuples = [('MSFT', 3), ('MSFT', 2), ('AAPL', 2), ('AAPL', 1),
- ('MSFT', 1)]
- midx = MultiIndex.from_tuples(tuples)
- df = DataFrame(np.random.rand(5, 4), index=midx)
- p = df.to_panel()
- assert_frame_equal(p.minor_xs(2), df.xs(2, level=1).sort_index())
- def test_to_excel(self):
- try:
- import xlwt # noqa
- import xlrd # noqa
- import openpyxl # noqa
- from pandas.io.excel import ExcelFile
- except ImportError:
- raise nose.SkipTest("need xlwt xlrd openpyxl")
- for ext in ['xls', 'xlsx']:
- with ensure_clean('__tmp__.' + ext) as path:
- self.panel.to_excel(path)
- try:
- reader = ExcelFile(path)
- except ImportError:
- raise nose.SkipTest("need xlwt xlrd openpyxl")
- for item, df in self.panel.iteritems():
- recdf = reader.parse(str(item), index_col=0)
- assert_frame_equal(df, recdf)
- def test_to_excel_xlsxwriter(self):
- try:
- import xlrd # noqa
- import xlsxwriter # noqa
- from pandas.io.excel import ExcelFile
- except ImportError:
- raise nose.SkipTest("Requires xlrd and xlsxwriter. Skipping test.")
- with ensure_clean('__tmp__.xlsx') as path:
- self.panel.to_excel(path, engine='xlsxwriter')
- try:
- reader = ExcelFile(path)
- except ImportError as e:
- raise nose.SkipTest("cannot write excel file: %s" % e)
- for item, df in self.panel.iteritems():
- recdf = reader.parse(str(item), index_col=0)
- assert_frame_equal(df, recdf)
- def test_dropna(self):
- p = Panel(np.random.randn(4, 5, 6), major_axis=list('abcde'))
- p.ix[:, ['b', 'd'], 0] = np.nan
- result = p.dropna(axis=1)
- exp = p.ix[:, ['a', 'c', 'e'], :]
- assert_panel_equal(result, exp)
- inp = p.copy()
- inp.dropna(axis=1, inplace=True)
- assert_panel_equal(inp, exp)
- result = p.dropna(axis=1, how='all')
- assert_panel_equal(result, p)
- p.ix[:, ['b', 'd'], :] = np.nan
- result = p.dropna(axis=1, how='all')
- exp = p.ix[:, ['a', 'c', 'e'], :]
- assert_panel_equal(result, exp)
- p = Panel(np.random.randn(4, 5, 6), items=list('abcd'))
- p.ix[['b'], :, 0] = np.nan
- result = p.dropna()
- exp = p.ix[['a', 'c', 'd']]
- assert_panel_equal(result, exp)
- result = p.dropna(how='all')
- assert_panel_equal(result, p)
- p.ix['b'] = np.nan
- result = p.dropna(how='all')
- exp = p.ix[['a', 'c', 'd']]
- assert_panel_equal(result, exp)
- def test_drop(self):
- df = DataFrame({"A": [1, 2], "B": [3, 4]})
- panel = Panel({"One": df, "Two": df})
- def check_drop(drop_val, axis_number, aliases, expected):
- try:
- actual = panel.drop(drop_val, axis=axis_number)
- assert_panel_equal(actual, expected)
- for alias in aliases:
- actual = panel.drop(drop_val, axis=alias)
- assert_panel_equal(actual, expected)
- except AssertionError:
- pprint_thing("Failed with axis_number %d and aliases: %s" %
- (axis_number, aliases))
- raise
- # Items
- expected = Panel({"One": df})
- check_drop('Two', 0, ['items'], expected)
- self.assertRaises(ValueError, panel.drop, 'Three')
- # errors = 'ignore'
- dropped = panel.drop('Three', errors='ignore')
- assert_panel_equal(dropped, panel)
- dropped = panel.drop(['Two', 'Three'], errors='ignore')
- expected = Panel({"One": df})
- assert_panel_equal(dropped, expected)
- # Major
- exp_df = DataFrame({"A": [2], "B": [4]}, index=[1])
- expected = Panel({"One": exp_df, "Two": exp_df})
- check_drop(0, 1, ['major_axis', 'major'], expected)
- exp_df = DataFrame({"A": [1], "B": [3]}, index=[0])
- expected = Panel({"One": exp_df, "Two": exp_df})
- check_drop([1], 1, ['major_axis', 'major'], expected)
- # Minor
- exp_df = df[['B']]
- expected = Panel({"One": exp_df, "Two": exp_df})
- check_drop(["A"], 2, ['minor_axis', 'minor'], expected)
- exp_df = df[['A']]
- expected = Panel({"One": exp_df, "Two": exp_df})
- check_drop("B", 2, ['minor_axis', 'minor'], expected)
- def test_update(self):
- pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
- other = Panel([[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])
- pan.update(other)
- expected = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
- [[3.6, 2., 3], [1.5, np.nan, 7], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
- assert_panel_equal(pan, expected)
- def test_update_from_dict(self):
- pan = Panel({'one': DataFrame([[1.5, np.nan, 3], [1.5, np.nan, 3],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]]),
- 'two': DataFrame([[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]])})
- other = {'two': DataFrame([[3.6, 2., np.nan], [np.nan, np.nan, 7]])}
- pan.update(other)
- expected = Panel(
- {'two': DataFrame([[3.6, 2., 3], [1.5, np.nan, 7],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]]),
- 'one': DataFrame([[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]])})
- assert_panel_equal(pan, expected)
- def test_update_nooverwrite(self):
- pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
- other = Panel([[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])
- pan.update(other, overwrite=False)
- expected = Panel([[[1.5, np.nan, 3], [1.5, np.nan, 3],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
- [[1.5, 2., 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
- assert_panel_equal(pan, expected)
- def test_update_filtered(self):
- pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
- other = Panel([[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])
- pan.update(other, filter_func=lambda x: x > 2)
- expected = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3], [1.5, np.nan, 7],
- [1.5, np.nan, 3.], [1.5, np.nan, 3.]]])
- assert_panel_equal(pan, expected)
- def test_update_raise(self):
- pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]],
- [[1.5, np.nan, 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3.],
- [1.5, np.nan, 3.]]])
- self.assertRaises(Exception, pan.update, *(pan, ),
- **{'raise_conflict': True})
- def test_all_any(self):
- self.assertTrue((self.panel.all(axis=0).values == nanall(
- self.panel, axis=0)).all())
- self.assertTrue((self.panel.all(axis=1).values == nanall(
- self.panel, axis=1).T).all())
- self.assertTrue((self.panel.all(axis=2).values == nanall(
- self.panel, axis=2).T).all())
- self.assertTrue((self.panel.any(axis=0).values == nanany(
- self.panel, axis=0)).all())
- self.assertTrue((self.panel.any(axis=1).values == nanany(
- self.panel, axis=1).T).all())
- self.assertTrue((self.panel.any(axis=2).values == nanany(
- self.panel, axis=2).T).all())
- def test_all_any_unhandled(self):
- self.assertRaises(NotImplementedError, self.panel.all, bool_only=True)
- self.assertRaises(NotImplementedError, self.panel.any, bool_only=True)
- class TestLongPanel(tm.TestCase):
- """
- LongPanel no longer exists, but...
- """
- _multiprocess_can_split_ = True
- def setUp(self):
- import warnings
- warnings.filterwarnings(action='ignore', category=FutureWarning)
- panel = tm.makePanel()
- tm.add_nans(panel)
- self.panel = panel.to_frame()
- self.unfiltered_panel = panel.to_frame(filter_observations=False)
- def test_ops_differently_indexed(self):
- # trying to set non-identically indexed panel
- wp = self.panel.to_panel()
- wp2 = wp.reindex(major=wp.major_axis[:-1])
- lp2 = wp2.to_frame()
- result = self.panel + lp2
- assert_frame_equal(result.reindex(lp2.index), lp2 * 2)
- # careful, mutation
- self.panel['foo'] = lp2['ItemA']
- assert_series_equal(self.panel['foo'].reindex(lp2.index), lp2['ItemA'],
- check_names=False)
- def test_ops_scalar(self):
- result = self.panel.mul(2)
- expected = DataFrame.__mul__(self.panel, 2)
- assert_frame_equal(result, expected)
- def test_combineFrame(self):
- wp = self.panel.to_panel()
- result = self.panel.add(wp['ItemA'].stack(), axis=0)
- assert_frame_equal(result.to_panel()['ItemA'], wp['ItemA'] * 2)
- def test_combinePanel(self):
- wp = self.panel.to_panel()
- result = self.panel.add(self.panel)
- wide_result = result.to_panel()
- assert_frame_equal(wp['ItemA'] * 2, wide_result['ItemA'])
- # one item
- result = self.panel.add(self.panel.filter(['ItemA']))
- def test_combine_scalar(self):
- result = self.panel.mul(2)
- expected = DataFrame(self.panel._data) * 2
- assert_frame_equal(result, expected)
- def test_combine_series(self):
- s = self.panel['ItemA'][:10]
- result = self.panel.add(s, axis=0)
- expected = DataFrame.add(self.panel, s, axis=0)
- assert_frame_equal(result, expected)
- s = self.panel.ix[5]
- result = self.panel + s
- expected = DataFrame.add(self.panel, s, axis=1)
- assert_frame_equal(result, expected)
- def test_operators(self):
- wp = self.panel.to_panel()
- result = (self.panel + 1).to_panel()
- assert_frame_equal(wp['ItemA'] + 1, result['ItemA'])
- def test_arith_flex_panel(self):
- ops = ['add', 'sub', 'mul', 'div', 'truediv', 'pow', 'floordiv', 'mod']
- if not compat.PY3:
- aliases = {}
- else:
- aliases = {'div': 'truediv'}
- self.panel = self.panel.to_panel()
- for n in [np.random.randint(-50, -1), np.random.randint(1, 50), 0]:
- for op in ops:
- alias = aliases.get(op, op)
- f = getattr(operator, alias)
- exp = f(self.panel, n)
- result = getattr(self.panel, op)(n)
- assert_panel_equal(result, exp, check_panel_type=True)
- # rops
- r_f = lambda x, y: f(y, x)
- exp = r_f(self.panel, n)
- result = getattr(self.panel, 'r' + op)(n)
- assert_panel_equal(result, exp)
- def test_sort(self):
- def is_sorted(arr):
- return (arr[1:] > arr[:-1]).any()
- sorted_minor = self.panel.sortlevel(level=1)
- self.assertTrue(is_sorted(sorted_minor.index.labels[1]))
- sorted_major = sorted_minor.sortlevel(level=0)
- self.assertTrue(is_sorted(sorted_major.index.labels[0]))
- def test_to_string(self):
- buf = StringIO()
- self.panel.to_string(buf)
- def test_to_sparse(self):
- if isinstance(self.panel, Panel):
- msg = 'sparsifying is not supported'
- tm.assertRaisesRegexp(NotImplementedError, msg,
- self.panel.to_sparse)
- def test_truncate(self):
- dates = self.panel.index.levels[0]
- start, end = dates[1], dates[5]
- trunced = self.panel.truncate(start, end).to_panel()
- expected = self.panel.to_panel()['ItemA'].truncate(start, end)
- # TODO trucate drops index.names
- assert_frame_equal(trunced['ItemA'], expected, check_names=False)
- trunced = self.panel.truncate(before=start).to_panel()
- expected = self.panel.to_panel()['ItemA'].truncate(before=start)
- # TODO trucate drops index.names
- assert_frame_equal(trunced['ItemA'], expected, check_names=False)
- trunced = self.panel.truncate(after=end).to_panel()
- expected = self.panel.to_panel()['ItemA'].truncate(after=end)
- # TODO trucate drops index.names
- assert_frame_equal(trunced['ItemA'], expected, check_names=False)
- # truncate on dates that aren't in there
- wp = self.panel.to_panel()
- new_index = wp.major_axis[::5]
- wp2 = wp.reindex(major=new_index)
- lp2 = wp2.to_frame()
- lp_trunc = lp2.truncate(wp.major_axis[2], wp.major_axis[-2])
- wp_trunc = wp2.truncate(wp.major_axis[2], wp.major_axis[-2])
- assert_panel_equal(wp_trunc, lp_trunc.to_panel())
- # throw proper exception
- self.assertRaises(Exception, lp2.truncate, wp.major_axis[-2],
- wp.major_axis[2])
- def test_axis_dummies(self):
- from pandas.core.reshape import make_axis_dummies
- minor_dummies = make_axis_dummies(self.panel, 'minor')
- self.assertEqual(len(minor_dummies.columns),
- len(self.panel.index.levels[1]))
- major_dummies = make_axis_dummies(self.panel, 'major')
- self.assertEqual(len(major_dummies.columns),
- len(self.panel.index.levels[0]))
- mapping = {'A': 'one', 'B': 'one', 'C': 'two', 'D': 'two'}
- transformed = make_axis_dummies(self.panel, 'minor',
- transform=mapping.get)
- self.assertEqual(len(transformed.columns), 2)
- self.assert_index_equal(transformed.columns, Index(['one', 'two']))
- # TODO: test correctness
- def test_get_dummies(self):
- from pandas.core.reshape import get_dummies, make_axis_dummies
- self.panel['Label'] = self.panel.index.labels[1]
- minor_dummies = make_axis_dummies(self.panel, 'minor')
- dummies = get_dummies(self.panel['Label'])
- self.assert_numpy_array_equal(dummies.values, minor_dummies.values)
- def test_mean(self):
- means = self.panel.mean(level='minor')
- # test versus Panel version
- wide_means = self.panel.to_panel().mean('major')
- assert_frame_equal(means, wide_means)
- def test_sum(self):
- sums = self.panel.sum(level='minor')
- # test versus Panel version
- wide_sums = self.panel.to_panel().sum('major')
- assert_frame_equal(sums, wide_sums)
- def test_count(self):
- index = self.panel.index
- major_count = self.panel.count(level=0)['ItemA']
- labels = index.labels[0]
- for i, idx in enumerate(index.levels[0]):
- self.assertEqual(major_count[i], (labels == i).sum())
- minor_count = self.panel.count(level=1)['ItemA']
- labels = index.labels[1]
- for i, idx in enumerate(index.levels[1]):
- self.assertEqual(minor_count[i], (labels == i).sum())
- def test_join(self):
- lp1 = self.panel.filter(['ItemA', 'ItemB'])
- lp2 = self.panel.filter(['ItemC'])
- joined = lp1.join(lp2)
- self.assertEqual(len(joined.columns), 3)
- self.assertRaises(Exception, lp1.join,
- self.panel.filter(['ItemB', 'ItemC']))
- def test_pivot(self):
- from pandas.core.reshape import _slow_pivot
- one, two, three = (np.array([1, 2, 3, 4, 5]),
- np.array(['a', 'b', 'c', 'd', 'e']),
- np.array([1, 2, 3, 5, 4.]))
- df = pivot(one, two, three)
- self.assertEqual(df['a'][1], 1)
- self.assertEqual(df['b'][2], 2)
- self.assertEqual(df['c'][3], 3)
- self.assertEqual(df['d'][4], 5)
- self.assertEqual(df['e'][5], 4)
- assert_frame_equal(df, _slow_pivot(one, two, three))
- # weird overlap, TODO: test?
- a, b, c = (np.array([1, 2, 3, 4, 4]),
- np.array(['a', 'a', 'a', 'a', 'a']),
- np.array([1., 2., 3., 4., 5.]))
- self.assertRaises(Exception, pivot, a, b, c)
- # corner case, empty
- df = pivot(np.array([]), np.array([]), np.array([]))
- def test_monotonic():
- pos = np.array([1, 2, 3, 5])
- def _monotonic(arr):
- return not (arr[1:] < arr[:-1]).any()
- assert _monotonic(pos)
- neg = np.array([1, 2, 3, 4, 3])
- assert not _monotonic(neg)
- neg2 = np.array([5, 1, 2, 3, 4, 5])
- assert not _monotonic(neg2)
- def test_panel_index():
- index = panelm.panel_index([1, 2, 3, 4], [1, 2, 3])
- expected = MultiIndex.from_arrays([np.tile([1, 2, 3, 4], 3),
- np.repeat([1, 2, 3], 4)],
- names=['time', 'panel'])
- tm.assert_index_equal(index, expected)
- if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)