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DEPR: remove v018 resample compatibilitiy
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closes #20554
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@jreback
jreback committed Apr 22, 2018
1 parent 8def649 commit 1a8ab25
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Showing 3 changed files with 13 additions and 281 deletions.
4 changes: 2 additions & 2 deletions doc/source/whatsnew/v0.23.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -555,7 +555,6 @@ Convert to an xarray DataArray
p.to_xarray()



.. _whatsnew_0230.api_breaking.core_common:

pandas.core.common removals
Expand Down Expand Up @@ -923,7 +922,8 @@ Removal of prior version deprecations/changes
- The ``infer_dst`` keyword in :meth:`Series.tz_localize`, :meth:`DatetimeIndex.tz_localize`
and :class:`DatetimeIndex` have been removed. ``infer_dst=True`` is equivalent to
``ambiguous='infer'``, and ``infer_dst=False`` to ``ambiguous='raise'`` (:issue:`7963`).

- When ``.resample()`` was changed from an eager to a lazy operation, like ``.groupby()`` in v0.18.0, we put in place compatibility (with a ``FutureWarning``),
so operations would continue to work. This is now fully removed, so a ``Resampler`` will no longer forward compat operations (:issue:`20554`)

.. _whatsnew_0230.performance:

Expand Down
126 changes: 10 additions & 116 deletions pandas/core/resample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,20 +62,6 @@ class Resampler(_GroupBy):
_attributes = ['freq', 'axis', 'closed', 'label', 'convention',
'loffset', 'base', 'kind']

# API compat of allowed attributes
_deprecated_valids = _attributes + ['__doc__', '_cache', '_attributes',
'binner', 'grouper', 'groupby',
'sort', 'kind', 'squeeze', 'keys',
'group_keys', 'as_index', 'exclusions',
'_groupby']

# don't raise deprecation warning on attributes starting with these
# patterns - prevents warnings caused by IPython introspection
_deprecated_valid_patterns = ['_ipython', '_repr']

# API compat of disallowed attributes
_deprecated_invalids = ['iloc', 'loc', 'ix', 'iat', 'at']

def __init__(self, obj, groupby=None, axis=0, kind=None, **kwargs):
self.groupby = groupby
self.keys = None
Expand All @@ -100,6 +86,16 @@ def __unicode__(self):
return "{klass} [{attrs}]".format(klass=self.__class__.__name__,
attrs=', '.join(attrs))

def __getattr__(self, attr):
if attr in self._internal_names_set:
return object.__getattribute__(self, attr)
if attr in self._attributes:
return getattr(self.groupby, attr)
if attr in self.obj:
return self[attr]

return object.__getattribute__(self, attr)

@property
def obj(self):
return self.groupby.obj
Expand All @@ -124,100 +120,6 @@ def _from_selection(self):
(self.groupby.key is not None or
self.groupby.level is not None))

def _deprecated(self, op):
warnings.warn(("\n.resample() is now a deferred operation\n"
"You called {op}(...) on this deferred object "
"which materialized it into a {klass}\nby implicitly "
"taking the mean. Use .resample(...).mean() "
"instead").format(op=op, klass=self._typ),
FutureWarning, stacklevel=3)
return self.mean()

def _make_deprecated_binop(op):
# op is a string

def _evaluate_numeric_binop(self, other):
result = self._deprecated(op)
return getattr(result, op)(other)
return _evaluate_numeric_binop

def _make_deprecated_unary(op, name):
# op is a callable

def _evaluate_numeric_unary(self):
result = self._deprecated(name)
return op(result)
return _evaluate_numeric_unary

def __array__(self):
return self._deprecated('__array__').__array__()

__gt__ = _make_deprecated_binop('__gt__')
__ge__ = _make_deprecated_binop('__ge__')
__lt__ = _make_deprecated_binop('__lt__')
__le__ = _make_deprecated_binop('__le__')
__eq__ = _make_deprecated_binop('__eq__')
__ne__ = _make_deprecated_binop('__ne__')

__add__ = __radd__ = _make_deprecated_binop('__add__')
__sub__ = __rsub__ = _make_deprecated_binop('__sub__')
__mul__ = __rmul__ = _make_deprecated_binop('__mul__')
__floordiv__ = __rfloordiv__ = _make_deprecated_binop('__floordiv__')
__truediv__ = __rtruediv__ = _make_deprecated_binop('__truediv__')
if not compat.PY3:
__div__ = __rdiv__ = _make_deprecated_binop('__div__')
__neg__ = _make_deprecated_unary(lambda x: -x, '__neg__')
__pos__ = _make_deprecated_unary(lambda x: x, '__pos__')
__abs__ = _make_deprecated_unary(lambda x: np.abs(x), '__abs__')
__inv__ = _make_deprecated_unary(lambda x: -x, '__inv__')

def __getattr__(self, attr):
if attr in self._internal_names_set:
return object.__getattribute__(self, attr)
if attr in self._attributes:
return getattr(self.groupby, attr)
if attr in self.obj:
return self[attr]

if attr in self._deprecated_invalids:
raise ValueError(".resample() is now a deferred operation\n"
"\tuse .resample(...).mean() instead of "
".resample(...)")

matches_pattern = any(attr.startswith(x) for x
in self._deprecated_valid_patterns)
if not matches_pattern and attr not in self._deprecated_valids:
# avoid the warning, if it's just going to be an exception
# anyway.
if not hasattr(self.obj, attr):
raise AttributeError("'{}' has no attribute '{}'".format(
type(self.obj).__name__, attr
))
self = self._deprecated(attr)

return object.__getattribute__(self, attr)

def __setattr__(self, attr, value):
if attr not in self._deprecated_valids:
raise ValueError("cannot set values on {0}".format(
self.__class__.__name__))
object.__setattr__(self, attr, value)

def __getitem__(self, key):
try:
return super(Resampler, self).__getitem__(key)
except (KeyError, AbstractMethodError):

# compat for deprecated
if isinstance(self.obj, ABCSeries):
return self._deprecated('__getitem__')[key]

raise

def __setitem__(self, attr, value):
raise ValueError("cannot set items on {0}".format(
self.__class__.__name__))

def _convert_obj(self, obj):
"""
provide any conversions for the object in order to correctly handle
Expand Down Expand Up @@ -282,11 +184,6 @@ def _assure_grouper(self):
def pipe(self, func, *args, **kwargs):
return super(Resampler, self).pipe(func, *args, **kwargs)

def plot(self, *args, **kwargs):
# for compat with prior versions, we want to
# have the warnings shown here and just have this work
return self._deprecated('plot').plot(*args, **kwargs)

_agg_doc = dedent("""
Examples
Expand Down Expand Up @@ -853,9 +750,6 @@ def size(self):
return result


Resampler._deprecated_valids += dir(Resampler)


# downsample methods
for method in ['sum', 'prod']:

Expand Down
164 changes: 1 addition & 163 deletions pandas/tests/test_resample.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,9 @@
import pandas as pd
import pandas.tseries.offsets as offsets
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas import (Series, DataFrame, Panel, Index, isna,
notna, Timestamp)

from pandas.core.dtypes.generic import ABCSeries, ABCDataFrame
from pandas.compat import range, lrange, zip, product, OrderedDict
from pandas.errors import UnsupportedFunctionCall
from pandas.core.groupby.groupby import DataError
Expand All @@ -28,8 +26,7 @@
from pandas.core.indexes.datetimes import date_range
from pandas.tseries.offsets import Minute, BDay
from pandas.core.indexes.period import period_range, PeriodIndex, Period
from pandas.core.resample import (DatetimeIndex, TimeGrouper,
DatetimeIndexResampler)
from pandas.core.resample import DatetimeIndex, TimeGrouper
from pandas.core.indexes.timedeltas import timedelta_range, TimedeltaIndex
from pandas.util.testing import (assert_series_equal, assert_almost_equal,
assert_frame_equal, assert_index_equal)
Expand Down Expand Up @@ -84,122 +81,6 @@ def test_api(self):
assert isinstance(result, DataFrame)
assert len(result) == 217

def test_api_changes_v018(self):

# change from .resample(....., how=...)
# to .resample(......).how()

r = self.series.resample('H')
assert isinstance(r, DatetimeIndexResampler)

for how in ['sum', 'mean', 'prod', 'min', 'max', 'var', 'std']:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = self.series.resample('H', how=how)
expected = getattr(self.series.resample('H'), how)()
tm.assert_series_equal(result, expected)

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = self.series.resample('H', how='ohlc')
expected = self.series.resample('H').ohlc()
tm.assert_frame_equal(result, expected)

# compat for pandas-like methods
for how in ['sort_values', 'isna']:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
getattr(r, how)()

# invalids as these can be setting operations
r = self.series.resample('H')
pytest.raises(ValueError, lambda: r.iloc[0])
pytest.raises(ValueError, lambda: r.iat[0])
pytest.raises(ValueError, lambda: r.loc[0])
pytest.raises(ValueError, lambda: r.loc[
Timestamp('2013-01-01 00:00:00', offset='H')])
pytest.raises(ValueError, lambda: r.at[
Timestamp('2013-01-01 00:00:00', offset='H')])

def f():
r[0] = 5

pytest.raises(ValueError, f)

# str/repr
r = self.series.resample('H')
with tm.assert_produces_warning(None):
str(r)
with tm.assert_produces_warning(None):
repr(r)

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
tm.assert_numpy_array_equal(np.array(r), np.array(r.mean()))

# masquerade as Series/DataFrame as needed for API compat
assert isinstance(self.series.resample('H'), ABCSeries)
assert not isinstance(self.frame.resample('H'), ABCSeries)
assert not isinstance(self.series.resample('H'), ABCDataFrame)
assert isinstance(self.frame.resample('H'), ABCDataFrame)

# bin numeric ops
for op in ['__add__', '__mul__', '__truediv__', '__div__', '__sub__']:

if getattr(self.series, op, None) is None:
continue
r = self.series.resample('H')

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
assert isinstance(getattr(r, op)(2), Series)

# unary numeric ops
for op in ['__pos__', '__neg__', '__abs__', '__inv__']:

if getattr(self.series, op, None) is None:
continue
r = self.series.resample('H')

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
assert isinstance(getattr(r, op)(), Series)

# comparison ops
for op in ['__lt__', '__le__', '__gt__', '__ge__', '__eq__', '__ne__']:
r = self.series.resample('H')

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
assert isinstance(getattr(r, op)(2), Series)

# IPython introspection shouldn't trigger warning GH 13618
for op in ['_repr_json', '_repr_latex',
'_ipython_canary_method_should_not_exist_']:
r = self.series.resample('H')
with tm.assert_produces_warning(None):
getattr(r, op, None)

# getitem compat
df = self.series.to_frame('foo')

# same as prior versions for DataFrame
pytest.raises(KeyError, lambda: df.resample('H')[0])

# compat for Series
# but we cannot be sure that we need a warning here
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = self.series.resample('H')[0]
expected = self.series.resample('H').mean()[0]
assert result == expected

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = self.series.resample('H')['2005-01-09 23:00:00']
expected = self.series.resample('H').mean()['2005-01-09 23:00:00']
assert result == expected

def test_groupby_resample_api(self):

# GH 12448
Expand Down Expand Up @@ -251,23 +132,6 @@ def test_pipe(self):
result = r.pipe(lambda x: x.max() - x.mean())
tm.assert_frame_equal(result, expected)

@td.skip_if_no_mpl
def test_plot_api(self):
# .resample(....).plot(...)
# hitting warnings
# GH 12448
s = Series(np.random.randn(60),
index=date_range('2016-01-01', periods=60, freq='1min'))
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = s.resample('15min').plot()
tm.assert_is_valid_plot_return_object(result)

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = s.resample('15min', how='sum').plot()
tm.assert_is_valid_plot_return_object(result)

def test_getitem(self):

r = self.frame.resample('H')
Expand Down Expand Up @@ -301,15 +165,6 @@ def test_attribute_access(self):
r = self.frame.resample('H')
tm.assert_series_equal(r.A.sum(), r['A'].sum())

# getting
pytest.raises(AttributeError, lambda: r.F)

# setting
def f():
r.F = 'bah'

pytest.raises(ValueError, f)

def test_api_compat_before_use(self):

# make sure that we are setting the binner
Expand Down Expand Up @@ -3012,23 +2867,6 @@ def setup_method(self, method):
freq='s',
periods=40))

def test_back_compat_v180(self):

df = self.frame
for how in ['sum', 'mean', 'prod', 'min', 'max', 'var', 'std']:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = df.groupby('A').resample('4s', how=how)
expected = getattr(df.groupby('A').resample('4s'), how)()
assert_frame_equal(result, expected)

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = df.groupby('A').resample('4s', how='mean',
fill_method='ffill')
expected = df.groupby('A').resample('4s').mean().ffill()
assert_frame_equal(result, expected)

def test_tab_complete_ipython6_warning(self, ip):
from IPython.core.completer import provisionalcompleter
code = dedent("""\
Expand Down

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