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DOC: Add more documentation showcasing CalendarDay #22633

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c580fc3
DOC: Add more docs about CalendarDay
Sep 8, 2018
f131230
change class to func
Sep 8, 2018
3da2c7d
add missing index
Sep 8, 2018
f0761c3
Merge remote-tracking branch 'upstream/master' into document_calendar…
Sep 13, 2018
3b8c102
Add more calendardaytests
Sep 13, 2018
12b4694
Merge remote-tracking branch 'upstream/master' into document_calendar…
Sep 13, 2018
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19 changes: 19 additions & 0 deletions doc/source/timeseries.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -900,12 +900,31 @@ calendar time arithmetic. :class:`CalendarDay` is useful preserving calendar day
semantics with date times with have day light savings transitions, i.e. :class:`CalendarDay`
will preserve the hour before the day light savings transition.

Addition with :class:`CalendarDay`:

.. ipython:: python

ts = pd.Timestamp('2016-10-30 00:00:00', tz='Europe/Helsinki')
ts + pd.offsets.Day(1)
ts + pd.offsets.CalendarDay(1)

Creating a :func:`date_range`:

.. ipython:: python

start = pd.Timestamp('2016-10-30 00:00:00', tz='Europe/Helsinki')
pd.date_range(start, freq='D', periods=3)
pd.date_range(start, freq='CD', periods=3)

Resampling a timeseries:

.. ipython:: python

idx = pd.date_range("2016-10-30", freq='H', periods=4*24, tz='Europe/Helsinki')
s = pd.Series(range(len(idx)), index=idx)
s.resample('D').count()
s.resample('CD').count()
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Is this supposed to work on master? After a clean build locally I get a ValueError: Invalid frequency: CD.

Don't think doctest covers these files; if it does then my mistake but figured it was worth asking

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This currently works on master:

In [3]: pd.__version__
Out[3]: '0.24.0.dev0+563.g0976e1261'

In [4]:    idx = pd.date_range("2016-10-30", freq='H', periods=4*24, tz='Europe/
   ...: Helsinki')
   ...:    s = pd.Series(range(len(idx)), index=idx)
   ...:    s.resample('D').count()
   ...:    s.resample('CD').count()
   ...:
   ...:
Out[4]:
2016-10-30 00:00:00+03:00    25
2016-10-31 00:00:00+02:00    24
2016-11-01 00:00:00+02:00    24
2016-11-02 00:00:00+02:00    23
Freq: CD, dtype: int64

I am not sure if the doctest uses master or the the latest tagged branch.



Parametric Offsets
~~~~~~~~~~~~~~~~~~
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22 changes: 21 additions & 1 deletion doc/source/whatsnew/v0.24.0.txt
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Expand Up @@ -317,14 +317,34 @@ and respect calendar day arithmetic while :class:`Day` and frequency alias ``'D'
will now respect absolute time (:issue:`22274`, :issue:`20596`, :issue:`16980`, :issue:`8774`)
See the :ref:`documentation here <timeseries.dayvscalendarday>` for more information.

Addition with :class:`CalendarDay` across a daylight savings time transition:
The difference between :class:`Day` vs :class:`CalendarDay` is most apparent
with timezone-aware datetime data with a daylight savings time transition:
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You say "most apparent with timezone-aware data". But is there a case where this is actually relevant for tz naive data?

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There is no difference between Day and CalendarDay for tz naive data. I've parameterized some tz naive data tests for 'D' and 'CD' to ensure this behavior.


Addition with :class:`CalendarDay`:

.. ipython:: python

ts = pd.Timestamp('2016-10-30 00:00:00', tz='Europe/Helsinki')
ts + pd.offsets.Day(1)
ts + pd.offsets.CalendarDay(1)

Creating a :func:`date_range`:

.. ipython:: python

start = pd.Timestamp('2016-10-30 00:00:00', tz='Europe/Helsinki')
pd.date_range(start, freq='D', periods=3)
pd.date_range(start, freq='CD', periods=3)

Resampling a timeseries:

.. ipython:: python

idx = pd.date_range("2016-10-30", freq='H', periods=4*24, tz='Europe/Helsinki')
s = pd.Series(range(len(idx)), index=idx)
s.resample('D').count()
s.resample('CD').count()

.. _whatsnew_0240.api_breaking.period_end_time:

Time values in ``dt.end_time`` and ``to_timestamp(how='end')``
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5 changes: 3 additions & 2 deletions pandas/tests/arrays/categorical/test_constructors.py
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Expand Up @@ -291,8 +291,9 @@ def test_constructor_with_datetimelike(self, dtl):
result = repr(c)
assert "NaT" in result

def test_constructor_from_index_series_datetimetz(self):
idx = date_range('2015-01-01 10:00', freq='D', periods=3,
@pytest.mark.parametrize('freq', ['CD', 'D'])
def test_constructor_from_index_series_datetimetz(self, freq):
idx = date_range('2015-01-01 10:00', freq=freq, periods=3,
tz='US/Eastern')
result = Categorical(idx)
tm.assert_index_equal(result.categories, idx)
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5 changes: 3 additions & 2 deletions pandas/tests/indexes/multi/test_partial_indexing.py
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Expand Up @@ -6,7 +6,8 @@
from pandas import DataFrame, MultiIndex, date_range


def test_partial_string_timestamp_multiindex():
@pytest.mark.parametrize('freq', ['D', 'CD'])
def test_partial_string_timestamp_multiindex(freq):
# GH10331
dr = pd.date_range('2016-01-01', '2016-01-03', freq='12H')
abc = ['a', 'b', 'c']
Expand Down Expand Up @@ -89,7 +90,7 @@ def test_partial_string_timestamp_multiindex():
df_swap.loc['2016-01-01']

# GH12685 (partial string with daily resolution or below)
dr = date_range('2013-01-01', periods=100, freq='D')
dr = date_range('2013-01-01', periods=100, freq=freq)
ix = MultiIndex.from_product([dr, ['a', 'b']])
df = DataFrame(np.random.randn(200, 1), columns=['A'], index=ix)

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5 changes: 3 additions & 2 deletions pandas/tests/io/formats/test_format.py
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Expand Up @@ -2497,11 +2497,12 @@ def test_date_nanos(self):
result = fmt.Datetime64Formatter(x).get_result()
assert result[0].strip() == "1970-01-01 00:00:00.000000200"

def test_dates_display(self):
@pytest.mark.parametrize('freq', ['CD', 'D'])
def test_dates_display(self, freq):

# 10170
# make sure that we are consistently display date formatting
x = Series(date_range('20130101 09:00:00', periods=5, freq='D'))
x = Series(date_range('20130101 09:00:00', periods=5, freq=freq))
x.iloc[1] = np.nan
result = fmt.Datetime64Formatter(x).get_result()
assert result[0].strip() == "2013-01-01 09:00:00"
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142 changes: 72 additions & 70 deletions pandas/tests/test_multilevel.py
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Expand Up @@ -71,9 +71,10 @@ def test_append(self):
result = a['A'].append(b['A'])
tm.assert_series_equal(result, self.frame['A'])

def test_append_index(self):
@pytest.mark.parametrize('freq', ['CD', 'D'])
def test_append_index(self, freq):
idx1 = Index([1.1, 1.2, 1.3])
idx2 = pd.date_range('2011-01-01', freq='D', periods=3,
idx2 = pd.date_range('2011-01-01', freq=freq, periods=3,
tz='Asia/Tokyo')
idx3 = Index(['A', 'B', 'C'])

Expand Down Expand Up @@ -2223,75 +2224,76 @@ def test_set_index_datetime(self):
tm.assert_index_equal(df.index.get_level_values(1), idx2)
tm.assert_index_equal(df.index.get_level_values(2), idx3)

def test_reset_index_datetime(self):
@pytest.mark.parametrize('freq', ['CD', 'D'])
@pytest.mark.parametrize('tz', ['UTC', 'Asia/Tokyo', 'US/Eastern'])
def test_reset_index_datetime(self, freq, tz):
# GH 3950
for tz in ['UTC', 'Asia/Tokyo', 'US/Eastern']:
idx1 = pd.date_range('1/1/2011', periods=5, freq='D', tz=tz,
name='idx1')
idx2 = Index(range(5), name='idx2', dtype='int64')
idx = MultiIndex.from_arrays([idx1, idx2])
df = DataFrame(
{'a': np.arange(5, dtype='int64'),
'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)

expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
datetime.datetime(2011, 1, 2),
datetime.datetime(2011, 1, 3),
datetime.datetime(2011, 1, 4),
datetime.datetime(2011, 1, 5)],
'idx2': np.arange(5, dtype='int64'),
'a': np.arange(5, dtype='int64'),
'b': ['A', 'B', 'C', 'D', 'E']},
columns=['idx1', 'idx2', 'a', 'b'])
expected['idx1'] = expected['idx1'].apply(
lambda d: Timestamp(d, tz=tz))

tm.assert_frame_equal(df.reset_index(), expected)

idx3 = pd.date_range('1/1/2012', periods=5, freq='MS',
tz='Europe/Paris', name='idx3')
idx = MultiIndex.from_arrays([idx1, idx2, idx3])
df = DataFrame(
{'a': np.arange(5, dtype='int64'),
'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)

expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
datetime.datetime(2011, 1, 2),
datetime.datetime(2011, 1, 3),
datetime.datetime(2011, 1, 4),
datetime.datetime(2011, 1, 5)],
'idx2': np.arange(5, dtype='int64'),
'idx3': [datetime.datetime(2012, 1, 1),
datetime.datetime(2012, 2, 1),
datetime.datetime(2012, 3, 1),
datetime.datetime(2012, 4, 1),
datetime.datetime(2012, 5, 1)],
'a': np.arange(5, dtype='int64'),
'b': ['A', 'B', 'C', 'D', 'E']},
columns=['idx1', 'idx2', 'idx3', 'a', 'b'])
expected['idx1'] = expected['idx1'].apply(
lambda d: Timestamp(d, tz=tz))
expected['idx3'] = expected['idx3'].apply(
lambda d: Timestamp(d, tz='Europe/Paris'))
tm.assert_frame_equal(df.reset_index(), expected)

# GH 7793
idx = MultiIndex.from_product([['a', 'b'], pd.date_range(
'20130101', periods=3, tz=tz)])
df = DataFrame(
np.arange(6, dtype='int64').reshape(
6, 1), columns=['a'], index=idx)

expected = DataFrame({'level_0': 'a a a b b b'.split(),
'level_1': [
datetime.datetime(2013, 1, 1),
datetime.datetime(2013, 1, 2),
datetime.datetime(2013, 1, 3)] * 2,
'a': np.arange(6, dtype='int64')},
columns=['level_0', 'level_1', 'a'])
expected['level_1'] = expected['level_1'].apply(
lambda d: Timestamp(d, freq='D', tz=tz))
tm.assert_frame_equal(df.reset_index(), expected)
idx1 = pd.date_range('1/1/2011', periods=5, freq=freq, tz=tz,
name='idx1')
idx2 = Index(range(5), name='idx2', dtype='int64')
idx = MultiIndex.from_arrays([idx1, idx2])
df = DataFrame(
{'a': np.arange(5, dtype='int64'),
'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)

expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
datetime.datetime(2011, 1, 2),
datetime.datetime(2011, 1, 3),
datetime.datetime(2011, 1, 4),
datetime.datetime(2011, 1, 5)],
'idx2': np.arange(5, dtype='int64'),
'a': np.arange(5, dtype='int64'),
'b': ['A', 'B', 'C', 'D', 'E']},
columns=['idx1', 'idx2', 'a', 'b'])
expected['idx1'] = expected['idx1'].apply(
lambda d: Timestamp(d, tz=tz))

tm.assert_frame_equal(df.reset_index(), expected)

idx3 = pd.date_range('1/1/2012', periods=5, freq='MS',
tz='Europe/Paris', name='idx3')
idx = MultiIndex.from_arrays([idx1, idx2, idx3])
df = DataFrame(
{'a': np.arange(5, dtype='int64'),
'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)

expected = DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
datetime.datetime(2011, 1, 2),
datetime.datetime(2011, 1, 3),
datetime.datetime(2011, 1, 4),
datetime.datetime(2011, 1, 5)],
'idx2': np.arange(5, dtype='int64'),
'idx3': [datetime.datetime(2012, 1, 1),
datetime.datetime(2012, 2, 1),
datetime.datetime(2012, 3, 1),
datetime.datetime(2012, 4, 1),
datetime.datetime(2012, 5, 1)],
'a': np.arange(5, dtype='int64'),
'b': ['A', 'B', 'C', 'D', 'E']},
columns=['idx1', 'idx2', 'idx3', 'a', 'b'])
expected['idx1'] = expected['idx1'].apply(
lambda d: Timestamp(d, tz=tz))
expected['idx3'] = expected['idx3'].apply(
lambda d: Timestamp(d, tz='Europe/Paris'))
tm.assert_frame_equal(df.reset_index(), expected)

# GH 7793
idx = MultiIndex.from_product([['a', 'b'], pd.date_range(
'20130101', periods=3, tz=tz)])
df = DataFrame(
np.arange(6, dtype='int64').reshape(
6, 1), columns=['a'], index=idx)

expected = DataFrame({'level_0': 'a a a b b b'.split(),
'level_1': [
datetime.datetime(2013, 1, 1),
datetime.datetime(2013, 1, 2),
datetime.datetime(2013, 1, 3)] * 2,
'a': np.arange(6, dtype='int64')},
columns=['level_0', 'level_1', 'a'])
expected['level_1'] = expected['level_1'].apply(
lambda d: Timestamp(d, freq=freq, tz=tz))
tm.assert_frame_equal(df.reset_index(), expected)

def test_reset_index_period(self):
# GH 7746
Expand Down
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