8000 BUG: Inconsistent return type for downsampling on resample of empty DataFrame by discort · Pull Request #15093 · pandas-dev/pandas · GitHub
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BUG: Inconsistent return type for downsampling on resample of empty DataFrame #15093

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Merged
merged 6 commits into from
Jun 13, 2017
Merged

BUG: Inconsistent return type for downsampling on resample of empty DataFrame #15093

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95d2008
BUG #14962
discort Jan 9, 2017
8f7b437
BUG: added series type to wrap_result for empty DataFrame
discort Jan 11, 2017
37ba820
added explicit 'size' method and defined logic there
discort Jan 25, 2017
43b2613
fixed lint warnings
discort Apr 5, 2017
c0e06b8
Updated tests for resampling of empty dataframes
discort Jun 12, 2017
44f52be
Fixed Jeff's comments
discort Jun 13, 2017
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added explicit 'size' method and defined logic there
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@discort
discort committed Jun 13, 2017
commit 37ba820ab2b59e258a9412cb7c0cf8d9723bd088
1 change: 0 additions & 1 deletion doc/source/whatsnew/v0.19.0.txt
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Expand Up @@ -1563,4 +1563,3 @@ Bug Fixes
- ``PeriodIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`)
- Bug in ``df.groupby`` where ``.median()`` returns arbitrary values if grouped dataframe contains empty bins (:issue:`13629`)
- Bug in ``Index.copy()`` where ``name`` parameter was ignored (:issue:`14302`)
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moves to 0.20.0

give a user friendly function here, _downsample is internal.

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revert this file

- Bug in ``_downsample()``. Inconsistent return type on resample of empty DataFrame (:issue:`14962`)
1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.20.0.txt
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Expand Up @@ -1685,6 +1685,7 @@ Groupby/Resample/Rolling
- Bug in ``.rolling()`` where ``pd.Timedelta`` or ``datetime.timedelta`` was not accepted as a ``window`` argument (:issue:`15440`)
- Bug in ``Rolling.quantile`` function that caused a segmentation fault when called with a quantile value outside of the range [0, 1] (:issue:`15463`)
- Bug in ``DataFrame.resample().median()`` if duplicate column names are present (:issue:`14233`)
- Bug in ``resample().size()``. Inconsistent return type on resample of empty DataFrame (:issue:`14962`)

Sparse
^^^^^^
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20 changes: 9 additions & 11 deletions pandas/core/resample.py
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Expand Up @@ -17,6 +17,7 @@
from pandas.core.indexes.period import PeriodIndex, period_range
import pandas.core.common as com
import pandas.core.algorithms as algos
from pandas.types.generic import ABCDataFrame

import pandas.compat as compat
from pandas.compat.numpy import function as nv
Expand Down Expand Up @@ -549,7 +550,13 @@ def var(self, ddof=1, *args, **kwargs):
nv.validate_resampler_func('var', args, kwargs)
return self._downsample('var', ddof=ddof)


@Appender(GroupBy.size.__doc__)
def size(self):
# It 'seems' special and needs extra handling. GH14962
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better comment here. say its a special case as higher level does returns a copy of 0-len objects.

result = self._downsample('size')
if not len(self.ax) and isinstance(self._selected_obj, ABCDataFrame):
result = pd.Series([], index=result.index, dtype='int64')
return result
Resampler._deprecated_valids += dir(Resampler)

# downsample methods
Expand All @@ -563,8 +570,7 @@ def f(self, _method=method, *args, **kwargs):
setattr(Resampler, method, f)

# groupby & aggregate methods
for method in ['count', 'size']:

for method in ['count']:
def f(self, _method=method):
return self._downsample(_method)
f.__doc__ = getattr(GroupBy, method).__doc__
Expand Down Expand Up @@ -770,14 +776,6 @@ def _wrap_result(self, result):
if self.kind == 'period' and not isinstance(result.index, PeriodIndex):
result.index = result.index.to_period(self.freq)

# Make consistent type of result. GH14962
if not len(self.ax):
grouper = BinGrouper([], result.index)
grouped = self._selected_obj.groupby(grouper)
result = pd.Series([],
index=result.index,
name=grouped.name,
dtype='int64')
return result


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21 changes: 9 additions & 12 deletions pandas/tests/test_resample.py
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Expand Up @@ -796,15 +796,22 @@ def test_resample_empty_dataframe(self):
methods = downsample_methods + upsample_methods
for method in methods:
result = getattr(f.resample(freq), method)()

expected = pd.Series([])
if method != 'size':
expected = f.copy()
assert_equal = assert_frame_equal
else:
# GH14962
expected = Series([])
assert_equal = assert_series_equal

expected.index = f.index._shallow_copy(freq=freq)
assert_index_equal(result.index, expected.index)
assert result.index.freq == expected.index.freq
assert_frame_equal(result, expected, check_dtype=False)

# test size for GH13212 (currently stays as df)


def test_resample_empty_dtypes(self):

# Empty series were sometimes causing a segfault (for the functions
Expand Down Expand Up @@ -858,16 +865,6 @@ def test_resample_loffset_arg_type(self):
assert_frame_equal(result_agg, expected)
assert_frame_equal(result_how, expected)

def test_resample_empty_dataframe_with_size(self):
# GH 14962
index = pd.DatetimeIndex([], freq='M')
df = pd.DataFrame([], index=index)

for freq in ['M', 'D', 'H']:
result = df.resample(freq).size()
expected = pd.Series([], index=index, dtype='int64')
assert_series_equal(result, expected)


class TestDatetimeIndex(Base):
_index_factory = lambda x: date_range
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