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/ tests / test_panel.py
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import OrderedDict, StringIO, lrange, range, signature
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import is_float_dtype
from pandas import (
DataFrame, Index, MultiIndex, Series, compat, date_range, isna, notna)
from pandas.core.nanops import nanall, nanany
import pandas.core.panel as panelm
from pandas.core.panel import Panel
import pandas.util.testing as tm
from pandas.util.testing import (
assert_almost_equal, assert_frame_equal, assert_panel_equal,
assert_series_equal, ensure_clean, makeCustomDataframe as mkdf,
makeMixedDataFrame)
from pandas.io.formats.printing import pprint_thing
from pandas.tseries.offsets import BDay, MonthEnd
def make_test_panel():
with catch_warnings(record=True):
simplefilter("ignore", FutureWarning)
_panel = tm.makePanel()
tm.add_nans(_panel)
_panel = _panel.copy()
return _panel
@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class PanelTests(object):
panel = None
def test_pickle(self):
unpickled = tm.round_trip_pickle(self.panel)
assert_frame_equal(unpickled['ItemA'], self.panel['ItemA'])
def test_rank(self):
pytest.raises(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]]]))
pytest.raises(TypeError, hash, c_empty)
pytest.raises(TypeError, hash, c)
@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class SafeForLongAndSparse(object):
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'
assert getattr(self.panel, attr).name is None
def test_iter(self):
tm.equalContents(list(self.panel), self.panel.items)
def test_count(self):
f = lambda s: notna(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, skipna_alternative=np.nansum)
def test_mean(self):
self._check_stat_op('mean', np.mean)
def test_prod(self):
self._check_stat_op('prod', np.prod, skipna_alternative=np.nanprod)
@pytest.mark.filterwarnings("ignore:Invalid value:RuntimeWarning")
@pytest.mark.filterwarnings("ignore:All-NaN:RuntimeWarning")
def test_median(self):
def wrapper(x):
if isna(x).any():
return np.nan
return np.median(x)
self._check_stat_op('median', wrapper)
@pytest.mark.filterwarnings("ignore:Invalid value:RuntimeWarning")
def test_min(self):
self._check_stat_op('min', np.min)
@pytest.mark.filterwarnings("ignore:Invalid value:RuntimeWarning")
def test_max(self):
self._check_stat_op('max', np.max)
@td.skip_if_no_scipy
def test_skew(self):
from scipy.stats import 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_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,
skipna_alternative=None):
if obj is None:
obj = self.panel
# # set some NAs
# obj.loc[5:10] = np.nan
# obj.loc[15:20, -2:] = np.nan
f = getattr(obj, name)
if has_skipna:
skipna_wrapper = tm._make_skipna_wrapper(alternative,
skipna_alternative)
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 name in ['sum', 'prod']:
assert_frame_equal(result, obj.apply(skipna_wrapper, axis=i))
pytest.raises(Exception, f, axis=obj.ndim)
# Unimplemented numeric_only parameter.
if 'numeric_only' in signature(f).args:
with pytest.raises(NotImplementedError, match=name):
f(numeric_only=True)
@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class SafeForSparse(object):
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'):
assert 'ItemA' not in self.panel._item_cache
assert self.panel.items is new_items
# TODO: unused?
item = self.panel[0] # noqa
self.panel.major_axis = new_major
assert self.panel[0].index is new_major
assert self.panel.major_axis is new_major
# TODO: unused?
item = self.panel[0] # noqa
self.panel.minor_axis = new_minor
assert self.panel[0].columns is new_minor
assert self.panel.minor_axis is new_minor
def test_get_axis_number(self):
assert self.panel._get_axis_number('items') == 0
assert self.panel._get_axis_number('major') == 1
assert self.panel._get_axis_number('minor') == 2
with pytest.raises(ValueError, match="No axis named foo"):
self.panel._get_axis_number('foo')
with pytest.raises(ValueError, match="No axis named foo"):
self.panel.__ge__(self.panel, axis='foo')
def test_get_axis_name(self):
assert self.panel._get_axis_name(0) == 'items'
assert self.panel._get_axis_name(1) == 'major_axis'
assert 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)
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
pytest.raises(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
assert 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 AttributeError:
pprint_thing("Failing operation: %r" % op)
raise
if compat.PY3:
try:
check_op(operator.truediv, 'div')
except AttributeError:
pprint_thing("Failing operation: %r" % 'div')
raise
def test_combinePanel(self):
result = self.panel.add(self.panel)
assert_panel_equal(result, self.panel * 2)
def test_neg(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=date_range('20130101', periods=4),
minor_axis=list('ABCDE'))
d = p.sum(axis=1).iloc[0]
ops = ['add', 'sub', 'mul', 'truediv',
'floordiv', 'div', 'mod', 'pow']
for op in ops:
with pytest.raises(NotImplementedError):
getattr(p, op)(d, axis=0)
def test_select(self):
p = self.panel
# select items
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = p.select(lambda x: x in ('ItemA', 'ItemC'), axis='items')
expected = p.reindex(items=['ItemA', 'ItemC'])
assert_panel_equal(result, expected)
# select major_axis
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
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)
assert_panel_equal(result, expected)
# select minor_axis
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = p.select(lambda x: x in ('D', 'A'), axis=2)
expected = p.reindex(minor=['A', 'D'])
assert_panel_equal(result, expected)
# corner case, empty thing
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = p.select(lambda x: x in ('foo', ), axis='items')
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:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
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)
assert_panel_equal(result, expected)
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)
assert result.name == 'A'
assert result2.name == 'A'
@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class CheckIndexing(object):
def test_getitem(self):
pytest.raises(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)
assert 'ItemA' not in self.panel.items
del self.panel['ItemB']
assert 'ItemB' not in self.panel.items
pytest.raises(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]
tm.assert_frame_equal(panelc[1], panel[1])
tm.assert_frame_equal(panelc[2], panel[2])
panelc = panel.copy()
del panelc[1]
tm.assert_frame_equal(panelc[0], panel[0])
tm.assert_frame_equal(panelc[2], panel[2])
panelc = panel.copy()
del panelc[2]
tm.assert_frame_equal(panelc[1], panel[1])
tm.assert_frame_equal(panelc[0], panel[0])
def test_setitem(self):
lp = self.panel.filter(['ItemA', 'ItemB']).to_frame()
with pytest.raises(TypeError):
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
assert self.panel['ItemG'].values.dtype == np.int64
assert self.panel['ItemE'].values.dtype == np.bool_
# object dtype
self.panel['ItemQ'] = 'foo'
assert self.panel['ItemQ'].values.dtype == np.object_
# boolean dtype
self.panel['ItemP'] = self.panel['ItemA'] > 0
assert self.panel['ItemP'].values.dtype == np.bool_
pytest.raises(TypeError, self.panel.__setitem__, 'foo',
self.panel.loc[['ItemP']])
# bad shape
p = Panel(np.random.randn(4, 3, 2))
msg = (r"shape of value must be \(3, 2\), "
r"shape of given object was \(4, 2\)")
with pytest.raises(ValueError, match=msg):
p[0] = np.random.randn(4, 2)
def test_setitem_ndarray(self):
timeidx = date_range(start=datetime(2009, 1, 1),
end=datetime(2009, 12, 31),
freq=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 = notna(panel.iloc[:, :, 0])
panel.loc[:, :, 'NewMinor'] = newminor
assert_frame_equal(panel.loc[:, :, 'NewMinor'],
newminor.astype(object))
newmajor = notna(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)
assert result.name == 'ItemA'
# not contained
idx = self.panel.major_axis[0] - BDay()
pytest.raises(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])
assert xs['ItemA'].dtype == np.float64
assert 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
pytest.raises(Exception, self.panel.minor_xs, 'E')
def test_minor_xs_mixed(self):
self.panel['ItemD'] = 'foo'
xs = self.panel.minor_xs('D')
assert xs['ItemA'].dtype == np.float64
assert xs['ItemD'].dtype == np.object_
def test_xs(self):
itemA = self.panel.xs('ItemA', axis=0)
expected = self.panel['ItemA']
tm.assert_frame_equal(itemA, expected)
# Get a view by default.
itemA_view = self.panel.xs('ItemA', axis=0)
itemA_view.values[:] = np.nan
assert np.isnan(self.panel['ItemA'].values).all()
# Mixed-type yields a copy.
self.panel['strings'] = 'foo'
result = self.panel.xs('D', axis=2)
assert result._is_copy is not None
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
with catch_warnings():
simplefilter("ignore", FutureWarning)
# XXX: warning in _validate_read_indexer
assert_panel_equal(p.loc[items, dates, cols],
p.reindex(items=items, major=dates, minor=cols))
# 2 specified
assert_panel_equal(p.loc[:, dates, cols],
p.reindex(major=dates, minor=cols))
assert_panel_equal(p.loc[items, :, cols],
p.reindex(items=items, minor=cols))
assert_panel_equal(p.loc[items, dates, :],
p.reindex(items=items, major=dates))
# only 1
assert_panel_equal(p.loc[items, :, :], p.reindex(items=items))
assert_panel_equal(p.loc[:, dates, :], p.reindex(major=dates))
assert_panel_equal(p.loc[:, :, cols], p.reindex(minor=cols))
def test_getitem_fancy_slice(self):
pass
def test_getitem_fancy_ints(self):
p = self.panel
# #1603
result = p.iloc[:, -1, :]
expected = p.loc[:, 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.loc[item], p[item])
assert_frame_equal(p.loc[item, :], p[item])
assert_frame_equal(p.loc[item, :, :], p[item])
# major axis, axis=1
assert_frame_equal(p.loc[:, date], p.major_xs(date))
assert_frame_equal(p.loc[:, date, :], p.major_xs(date))
# minor axis, axis=2
assert_frame_equal(p.loc[:, :, 'C'], p.minor_xs('C'))
# get Series
assert_series_equal(p.loc[item, date], p[item].loc[date])
assert_series_equal(p.loc[item, date, :], p[item].loc[date])
assert_series_equal(p.loc[item, :, col], p[item][col])
assert_series_equal(p.loc[:, date, col], p.major_xs(date).loc[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.loc[:, 22, [111, 333]] = b
assert_frame_equal(a.loc[:, 22, [111, 333]], b)
def test_ix_align(self):
from pandas import Series
b = Series(np.random.randn(10), name=0)
b.sort_values()
df_orig = Panel(np.random.randn(3, 10, 2))
df = df_orig.copy()
df.loc[0, :, 0] = b
assert_series_equal(df.loc[0, :, 0].reindex(b.index), b)
df = df_orig.swapaxes(0, 1)
df.loc[:, 0, 0] = b
assert_series_equal(df.loc[:, 0, 0].reindex(b.index), b)
df = df_orig.swapaxes(1, 2)
df.loc[0, 0, :] = b
assert_series_equal(df.loc[0, 0, :].reindex(b.index), b)
def test_ix_frame_align(self):
p_orig = tm.makePanel()
df = p_orig.iloc[0].copy()
assert_frame_equal(p_orig['ItemA'], df)
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.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.iloc[0, [0, 1, 3, 5], -2:] = df
out = p.iloc[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.loc[indexer]
obj.values[:] = 0
assert (obj.values == 0).all()
comp(cp.loc[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):
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)
tm.assert_numpy_array_equal(result.values,
func(p1.values, p2.values))
# versus non-indexed same objs
pytest.raises(Exception, func, p1, tp)
# versus different objs
pytest.raises(Exception, func, p1, df)
# versus scalar
result3 = func(self.panel, 0)
tm.assert_numpy_array_equal(result3.values,
func(self.panel.values, 0))
with np.errstate(invalid='ignore'):
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:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = self.panel.get_value(item, mjr, mnr)
expected = self.panel[item][mnr][mjr]
assert_almost_equal(result, expected)
with catch_warnings():
simplefilter("ignore", FutureWarning)
msg = "There must be an argument for each axis"
with pytest.raises(TypeError, match=msg):
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:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
self.panel.set_value(item, mjr, mnr, 1.)
tm.assert_almost_equal(self.panel[item][mnr][mjr], 1.)
# resize
with catch_warnings():
simplefilter("ignore", FutureWarning)
res = self.panel.set_value('ItemE', 'foo', 'bar', 1.5)
assert isinstance(res, Panel)
assert res is not self.panel
assert res.get_value('ItemE', 'foo', 'bar') == 1.5
res3 = self.panel.set_value('ItemE', 'foobar', 'baz', 5)
assert is_float_dtype(res3['ItemE'].values)
msg = ("There must be an argument for each "
"axis plus the value provided")
with pytest.raises(TypeError, match=msg):
self.panel.set_value('a')
@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestPanel(PanelTests, CheckIndexing, SafeForLongAndSparse,
SafeForSparse):
def setup_method(self, method):
self.panel = make_test_panel()
self.panel.major_axis.name = None
self.panel.minor_axis.name = None
self.panel.items.name = None
def test_constructor(self):
# with BlockManager
wp = Panel(self.panel._data)
assert wp._data is self.panel._data
wp = Panel(self.panel._data, copy=True)
assert wp._data is not self.panel._data
tm.assert_panel_equal(wp, self.panel)
# strings handled prop
wp = Panel([[['foo', 'foo', 'foo', ], ['foo', 'foo', 'foo']]])
assert wp.values.dtype == np.object_
vals = self.panel.values
# no copy
wp = Panel(vals)
assert wp.values is vals
# copy
wp = Panel(vals, copy=True)
assert wp.values is not 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)
tm.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)
tm.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']]]
pytest.raises(ValueError, Panel, data, dtype=float)
def test_constructor_empty_panel(self):
empty = Panel()
assert len(empty.items) == 0
assert len(empty.major_axis) == 0
assert 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')
assert panel.values.dtype == np.object_
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)
def test_constructor_fails_with_not_3d_input(self):
msg = "The number of dimensions required is 3"
with pytest.raises(ValueError, match=msg):
Panel(np.random.randn(10, 2))
def test_consolidate(self):
assert self.panel._data.is_consolidated()
self.panel['foo'] = 1.
assert not self.panel._data.is_consolidated()
panel = self.panel._consolidate()
assert 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
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)
def test_constructor_dict_mixed(self):
data = {k: v.values for k, v in self.panel.iteritems()}
result = Panel(data)
exp_major = Index(np.arange(len(self.panel.major_axis)))
tm.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]
pytest.raises(Exception, Panel, data)
data['ItemB'] = self.panel['ItemB'].values[:, :-1]
pytest.raises(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)
assert list(p.items) == keys
p = Panel.from_dict(d)
assert 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')
assert panel['foo'].values.dtype == np.object_
assert panel['A'].values.dtype == np.float64
def test_constructor_error_msgs(self):
msg = (r"Shape of passed values is \(3, 4, 5\), "
r"indices imply \(4, 5, 5\)")
with pytest.raises(ValueError, match=msg):
Panel(np.random.randn(3, 4, 5),
lrange(4), lrange(5), lrange(5))
msg = (r"Shape of passed values is \(3, 4, 5\), "
r"indices imply \(5, 4, 5\)")
with pytest.raises(ValueError, match=msg):
Panel(np.random.randn(3, 4, 5),
lrange(5), lrange(4), lrange(5))
msg = (r"Shape of passed values is \(3, 4, 5\), "
r"indices imply \(5, 5, 4\)")
with pytest.raises(ValueError, match=msg):
Panel(np.random.randn(3, 4, 5),
lrange(5), lrange(5), lrange(4))
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)
pytest.raises(NotImplementedError, panel.astype, {0: str})
def test_apply(self):
# GH1148
# ufunc
applied = self.panel.apply(np.sqrt)
with np.errstate(invalid='ignore'):
expected = np.sqrt(self.panel.values)
assert_almost_equal(applied.values, expected)
# 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
result = self.panel.apply(f, axis=['items', 'major_axis'])
expected = Panel({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({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({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
pytest.raises(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))
# raise exception put both major and major_axis
pytest.raises(Exception, self.panel.reindex,
minor_axis=new_minor,
minor=new_minor)
# this ok
result = self.panel.reindex()
assert_panel_equal(result, self.panel)
assert result is not 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)
assert result is self.panel
def test_reindex_axis_style(self):
panel = Panel(np.random.rand(5, 5, 5))
expected0 = Panel(panel.values).iloc[[0, 1]]
expected1 = Panel(panel.values).iloc[:, [0, 1]]
expected2 = Panel(panel.values).iloc[:, :, [0, 1]]
result = panel.reindex([0, 1], axis=0)
assert_panel_equal(result, expected0)
result = panel.reindex([0, 1], axis=1)
assert_panel_equal(result, expected1)
result = panel.reindex([0, 1], axis=2)
assert_panel_equal(result, expected2)
result = panel.reindex([0, 1], axis=2)
assert_panel_equal(result, expected2)
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)
assert result.items is self.panel.items
assert result.major_axis is self.panel.major_axis
assert result.minor_axis is 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 indices 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)
pytest.raises(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)
assert 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)
pytest.raises(ValueError, self.panel.fillna)
pytest.raises(ValueError, self.panel.fillna, 5, method='ffill')
pytest.raises(TypeError, self.panel.fillna, [1, 2])
pytest.raises(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
pytest.raises(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')
assert result.items is self.panel.minor_axis
result = self.panel.swapaxes('items', 'major')
assert result.items is self.panel.major_axis
result = self.panel.swapaxes('major', 'minor')
assert result.major_axis is 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)
assert result.items is self.panel.major_axis
# this works, but return a copy
result = self.panel.swapaxes('items', 'items')
assert_panel_equal(self.panel, result)
assert 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 pytest.raises(TypeError,
match='not enough/duplicate arguments'):
self.panel.transpose('minor', maj='major', minor='items')
with pytest.raises(ValueError,
match='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)
pytest.raises(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
assert notna(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
assert 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()
assert rdf.index.names == df.index.names
assert 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()
assert 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']],
codes=[[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()
assert isna(panel[0].loc[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'])
with pytest.raises(ValueError, match='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({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)
with pytest.raises(ValueError, match='does not match'):
ps.tshift(freq='M')
# DatetimeIndex
panel = make_test_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.iloc[:, [0, 5, 7], :]
pytest.raises(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=date_range('1/1/2000', periods=5),
minor_axis=['A', 'B'])
expected = Panel(evalues, items=['Item1', 'Item2'],
major_axis=date_range('1/1/2000', periods=5),
minor_axis=['A', 'B'])
result = p.round()
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=date_range('1/1/2000', periods=5),
minor_axis=['A', 'B'])
expected = Panel(evalues, items=['Item1', 'Item2'],
major_axis=date_range('1/1/2000', periods=5),
minor_axis=['A', 'B'])
result = np.round(p)
assert_panel_equal(expected, result)
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.round(p, out=p)
# removing Panel before NumPy enforces, so just ignore
@pytest.mark.filterwarnings("ignore:Using a non-tuple:FutureWarning")
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.loc['a']
assert_panel_equal(f1, f2)
assert (f1.items == [1, 2]).all()
assert (f2.items == [1, 2]).all()
MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)],
names=['first', 'second'])
@pytest.mark.filterwarnings("ignore:Using a non-tuple:FutureWarning")
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']
assert (f1.items == [1, 2]).all()
f1 = wp[('b', 1)]
assert (f1.columns == ['A', 'B', 'C', 'D']).all()
def test_repr_empty(self):
empty = Panel()
repr(empty)
# ignore warning from us, because removing panel
@pytest.mark.filterwarnings("ignore:Using:FutureWarning")
def test_rename(self):
mapper = {'ItemA': 'foo', 'ItemB': 'bar', 'ItemC': 'baz'}
renamed = self.panel.rename(items=mapper)
exp = Index(['foo', 'bar', 'baz'])
tm.assert_index_equal(renamed.items, exp)
renamed = self.panel.rename(minor_axis=str.lower)
exp = Index(['a', 'b', 'c', 'd'])
tm.assert_index_equal(renamed.minor_axis, exp)
# don't copy
renamed_nocopy = self.panel.rename(items=mapper, copy=False)
renamed_nocopy['foo'] = 3.
assert (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:
pytest.skip("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:
pytest.skip("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:
pytest.skip("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:
pytest.skip("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)
@pytest.mark.filterwarnings("ignore:'.reindex:FutureWarning")
def test_dropna(self):
p = Panel(np.random.randn(4, 5, 6), major_axis=list('abcde'))
p.loc[:, ['b', 'd'], 0] = np.nan
result = p.dropna(axis=1)
exp = p.loc[:, ['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.loc[:, ['b', 'd'], :] = np.nan
result = p.dropna(axis=1, how='all')
exp = p.loc[:, ['a', 'c', 'e'], :]
assert_panel_equal(result, exp)
p = Panel(np.random.randn(4, 5, 6), items=list('abcd'))
p.loc[['b'], :, 0] = np.nan
result = p.dropna()
exp = p.loc[['a', 'c', 'd']]
assert_panel_equal(result, exp)
result = p.dropna(how='all')
assert_panel_equal(result, p)
p.loc['b'] = np.nan
result = p.dropna(how='all')
exp = p.loc[['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)
pytest.raises(KeyError, 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(
{'one': DataFrame([[1.5, np.nan, 3.],
[1.5, np.nan, 3.],
[1.5, np.nan, 3.],
[1.5, np.nan, 3.]]),
'two': DataFrame([[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_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)
@pytest.mark.parametrize('bad_kwarg, exception, msg', [
# errors must be 'ignore' or 'raise'
({'errors': 'something'}, ValueError, 'The parameter errors must.*'),
({'join': 'inner'}, NotImplementedError, 'Only left join is supported')
])
def test_update_raise_bad_parameter(self, bad_kwarg, exception, msg):
pan = Panel([[[1.5, np.nan, 3.]]])
with pytest.raises(exception, match=msg):
pan.update(pan, **bad_kwarg)
def test_update_raise_on_overlap(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.]]])
with pytest.raises(ValueError, match='Data overlaps'):
pan.update(pan, errors='raise')
@pytest.mark.parametrize('raise_conflict', [True, False])
def test_update_deprecation(self, raise_conflict):
pan = Panel([[[1.5, np.nan, 3.]]])
other = Panel([[[]]])
with tm.assert_produces_warning(FutureWarning):
pan.update(other, raise_conflict=raise_conflict)
def test_all_any(self):
assert (self.panel.all(axis=0).values == nanall(
self.panel, axis=0)).all()
assert (self.panel.all(axis=1).values == nanall(
self.panel, axis=1).T).all()
assert (self.panel.all(axis=2).values == nanall(
self.panel, axis=2).T).all()
assert (self.panel.any(axis=0).values == nanany(
self.panel, axis=0)).all()
assert (self.panel.any(axis=1).values == nanany(
self.panel, axis=1).T).all()
assert (self.panel.any(axis=2).values == nanany(
self.panel, axis=2).T).all()
def test_all_any_unhandled(self):
pytest.raises(NotImplementedError, self.panel.all, bool_only=True)
pytest.raises(NotImplementedError, self.panel.any, bool_only=True)
# GH issue 15960
def test_sort_values(self):
pytest.raises(NotImplementedError, self.panel.sort_values)
pytest.raises(NotImplementedError, self.panel.sort_values, 'ItemA')
@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
class TestPanelFrame(object):
"""
Check that conversions to and from Panel to DataFrame work.
"""
def setup_method(self, method):
panel = make_test_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.iloc[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.sort_index(level=1)
assert is_sorted(sorted_minor.index.codes[1])
sorted_major = sorted_minor.sort_index(level=0)
assert is_sorted(sorted_major.index.codes[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'
with pytest.raises(NotImplementedError, match=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 truncate 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 truncate 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 truncate 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
pytest.raises(Exception, lp2.truncate, wp.major_axis[-2],
wp.major_axis[2])
def test_axis_dummies(self):
from pandas.core.reshape.reshape import make_axis_dummies
minor_dummies = make_axis_dummies(self.panel, 'minor').astype(np.uint8)
assert len(minor_dummies.columns) == len(self.panel.index.levels[1])
major_dummies = make_axis_dummies(self.panel, 'major').astype(np.uint8)
assert 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).astype(np.uint8)
assert len(transformed.columns) == 2
tm.assert_index_equal(transformed.columns, Index(['one', 'two']))
# TODO: test correctness
def test_get_dummies(self):
from pandas.core.reshape.reshape import get_dummies, make_axis_dummies
self.panel['Label'] = self.panel.index.codes[1]
minor_dummies = make_axis_dummies(self.panel, 'minor').astype(np.uint8)
dummies = get_dummies(self.panel['Label'])
tm.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']
level_codes = index.codes[0]
for i, idx in enumerate(index.levels[0]):
assert major_count[i] == (level_codes == i).sum()
minor_count = self.panel.count(level=1)['ItemA']
level_codes = index.codes[1]
for i, idx in enumerate(index.levels[1]):
assert minor_count[i] == (level_codes == i).sum()
def test_join(self):
lp1 = self.panel.filter(['ItemA', 'ItemB'])
lp2 = self.panel.filter(['ItemC'])
joined = lp1.join(lp2)
assert len(joined.columns) == 3
pytest.raises(Exception, lp1.join,
self.panel.filter(['ItemB', 'ItemC']))
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)
@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_panel_np_all():
wp = Panel({"A": DataFrame({'b': [1, 2]})})
result = np.all(wp)
assert result == np.bool_(True)