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test_ops.py
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import pytest
import warnings
import numpy as np
from datetime import datetime
import pandas as pd
import pandas._libs.tslib as tslib
import pandas.util.testing as tm
from pandas import (DatetimeIndex, PeriodIndex, Series, Timestamp,
date_range, bdate_range, Index)
from pandas.tseries.offsets import BMonthEnd, CDay, BDay, Day, Hour
from pandas.tests.test_base import Ops
from pandas.core.dtypes.generic import ABCDateOffset
START, END = datetime(2009, 1, 1), datetime(2010, 1, 1)
class TestDatetimeIndexOps(Ops):
def setup_method(self, method):
super(TestDatetimeIndexOps, self).setup_method(method)
mask = lambda x: (isinstance(x, DatetimeIndex) or
isinstance(x, PeriodIndex))
self.is_valid_objs = [o for o in self.objs if mask(o)]
self.not_valid_objs = [o for o in self.objs if not mask(o)]
def test_ops_properties(self):
f = lambda x: isinstance(x, DatetimeIndex)
self.check_ops_properties(DatetimeIndex._field_ops, f)
self.check_ops_properties(DatetimeIndex._object_ops, f)
self.check_ops_properties(DatetimeIndex._bool_ops, f)
def test_ops_properties_basic(self):
# sanity check that the behavior didn't change
# GH7206
for op in ['year', 'day', 'second', 'weekday']:
pytest.raises(TypeError, lambda x: getattr(self.dt_series, op))
# attribute access should still work!
s = Series(dict(year=2000, month=1, day=10))
assert s.year == 2000
assert s.month == 1
assert s.day == 10
pytest.raises(AttributeError, lambda: s.weekday)
def test_minmax_tz(self, tz_naive_fixture):
tz = tz_naive_fixture
# monotonic
idx1 = pd.DatetimeIndex(['2011-01-01', '2011-01-02',
'2011-01-03'], tz=tz)
assert idx1.is_monotonic
# non-monotonic
idx2 = pd.DatetimeIndex(['2011-01-01', pd.NaT, '2011-01-03',
'2011-01-02', pd.NaT], tz=tz)
assert not idx2.is_monotonic
for idx in [idx1, idx2]:
assert idx.min() == Timestamp('2011-01-01', tz=tz)
assert idx.max() == Timestamp('2011-01-03', tz=tz)
assert idx.argmin() == 0
assert idx.argmax() == 2
@pytest.mark.parametrize('op', ['min', 'max'])
def test_minmax_nat(self, op):
# Return NaT
obj = DatetimeIndex([])
assert pd.isna(getattr(obj, op)())
obj = DatetimeIndex([pd.NaT])
assert pd.isna(getattr(obj, op)())
obj = DatetimeIndex([pd.NaT, pd.NaT, pd.NaT])
assert pd.isna(getattr(obj, op)())
def test_numpy_minmax(self):
dr = pd.date_range(start='2016-01-15', end='2016-01-20')
assert np.min(dr) == Timestamp('2016-01-15 00:00:00', freq='D')
assert np.max(dr) == Timestamp('2016-01-20 00:00:00', freq='D')
errmsg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, errmsg, np.min, dr, out=0)
tm.assert_raises_regex(ValueError, errmsg, np.max, dr, out=0)
assert np.argmin(dr) == 0
assert np.argmax(dr) == 5
errmsg = "the 'out' parameter is not supported"
tm.assert_raises_regex(
ValueError, errmsg, np.argmin, dr, out=0)
tm.assert_raises_regex(
ValueError, errmsg, np.argmax, dr, out=0)
def test_repeat_range(self, tz_naive_fixture):
tz = tz_naive_fixture
rng = date_range('1/1/2000', '1/1/2001')
result = rng.repeat(5)
assert result.freq is None
assert len(result) == 5 * len(rng)
index = pd.date_range('2001-01-01', periods=2, freq='D', tz=tz)
exp = pd.DatetimeIndex(['2001-01-01', '2001-01-01',
'2001-01-02', '2001-01-02'], tz=tz)
for res in [index.repeat(2), np.repeat(index, 2)]:
tm.assert_index_equal(res, exp)
assert res.freq is None
index = pd.date_range('2001-01-01', periods=2, freq='2D', tz=tz)
exp = pd.DatetimeIndex(['2001-01-01', '2001-01-01',
'2001-01-03', '2001-01-03'], tz=tz)
for res in [index.repeat(2), np.repeat(index, 2)]:
tm.assert_index_equal(res, exp)
assert res.freq is None
index = pd.DatetimeIndex(['2001-01-01', 'NaT', '2003-01-01'],
tz=tz)
exp = pd.DatetimeIndex(['2001-01-01', '2001-01-01', '2001-01-01',
'NaT', 'NaT', 'NaT',
'2003-01-01', '2003-01-01', '2003-01-01'],
tz=tz)
for res in [index.repeat(3), np.repeat(index, 3)]:
tm.assert_index_equal(res, exp)
assert res.freq is None
def test_repeat(self, tz_naive_fixture):
tz = tz_naive_fixture
reps = 2
msg = "the 'axis' parameter is not supported"
rng = pd.date_range(start='2016-01-01', periods=2,
freq='30Min', tz=tz)
expected_rng = DatetimeIndex([
Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'),
Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'),
Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'),
Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'),
])
res = rng.repeat(reps)
tm.assert_index_equal(res, expected_rng)
assert res.freq is None
tm.assert_index_equal(np.repeat(rng, reps), expected_rng)
tm.assert_raises_regex(ValueError, msg, np.repeat,
rng, reps, axis=1)
def test_resolution(self, tz_naive_fixture):
tz = tz_naive_fixture
for freq, expected in zip(['A', 'Q', 'M', 'D', 'H', 'T',
'S', 'L', 'U'],
['day', 'day', 'day', 'day', 'hour',
'minute', 'second', 'millisecond',
'microsecond']):
idx = pd.date_range(start='2013-04-01', periods=30, freq=freq,
tz=tz)
assert idx.resolution == expected
def test_value_counts_unique(self, tz_naive_fixture):
tz = tz_naive_fixture
# GH 7735
idx = pd.date_range('2011-01-01 09:00', freq='H', periods=10)
# create repeated values, 'n'th element is repeated by n+1 times
idx = DatetimeIndex(np.repeat(idx.values, range(1, len(idx) + 1)),
tz=tz)
exp_idx = pd.date_range('2011-01-01 18:00', freq='-1H', periods=10,
tz=tz)
expected = Series(range(10, 0, -1), index=exp_idx, dtype='int64')
for obj in [idx, Series(idx)]:
tm.assert_series_equal(obj.value_counts(), expected)
expected = pd.date_range('2011-01-01 09:00', freq='H', periods=10,
tz=tz)
tm.assert_index_equal(idx.unique(), expected)
idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 09:00',
'2013-01-01 09:00', '2013-01-01 08:00',
'2013-01-01 08:00', pd.NaT], tz=tz)
exp_idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 08:00'],
tz=tz)
expected = Series([3, 2], index=exp_idx)
for obj in [idx, Series(idx)]:
tm.assert_series_equal(obj.value_counts(), expected)
exp_idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 08:00',
pd.NaT], tz=tz)
expected = Series([3, 2, 1], index=exp_idx)
for obj in [idx, Series(idx)]:
tm.assert_series_equal(obj.value_counts(dropna=False),
expected)
tm.assert_index_equal(idx.unique(), exp_idx)
def test_nonunique_contains(self):
# GH 9512
for idx in map(DatetimeIndex,
([0, 1, 0], [0, 0, -1], [0, -1, -1],
['2015', '2015', '2016'], ['2015', '2015', '2014'])):
assert idx[0] in idx
@pytest.mark.parametrize('idx',
[
DatetimeIndex(
['2011-01-01',
'2011-01-02',
'2011-01-03'],
freq='D', name='idx'),
DatetimeIndex(
['2011-01-01 09:00',
'2011-01-01 10:00',
'2011-01-01 11:00'],
freq='H', name='tzidx', tz='Asia/Tokyo')
])
def test_order_with_freq(self, idx):
ordered = idx.sort_values()
tm.assert_index_equal(ordered, idx)
assert ordered.freq == idx.freq
ordered = idx.sort_values(ascending=False)
expected = idx[::-1]
tm.assert_index_equal(ordered, expected)
assert ordered.freq == expected.freq
assert ordered.freq.n == -1
ordered, indexer = idx.sort_values(return_indexer=True)
tm.assert_index_equal(ordered, idx)
tm.assert_numpy_array_equal(indexer, np.array([0, 1, 2]),
check_dtype=False)
assert ordered.freq == idx.freq
ordered, indexer = idx.sort_values(return_indexer=True,
ascending=False)
expected = idx[::-1]
tm.assert_index_equal(ordered, expected)
tm.assert_numpy_array_equal(indexer,
np.array([2, 1, 0]),
check_dtype=False)
assert ordered.freq == expected.freq
assert ordered.freq.n == -1
@pytest.mark.parametrize('index_dates,expected_dates', [
(['2011-01-01', '2011-01-03', '2011-01-05',
'2011-01-02', '2011-01-01'],
['2011-01-01', '2011-01-01', '2011-01-02',
'2011-01-03', '2011-01-05']),
(['2011-01-01', '2011-01-03', '2011-01-05',
'2011-01-02', '2011-01-01'],
['2011-01-01', '2011-01-01', '2011-01-02',
'2011-01-03', '2011-01-05']),
([pd.NaT, '2011-01-03', '2011-01-05',
'2011-01-02', pd.NaT],
[pd.NaT, pd.NaT, '2011-01-02', '2011-01-03',
'2011-01-05'])
])
def test_order_without_freq(self, index_dates, expected_dates,
tz_naive_fixture):
tz = tz_naive_fixture
# without freq
index = DatetimeIndex(index_dates, tz=tz, name='idx')
expected = DatetimeIndex(expected_dates, tz=tz, name='idx')
ordered = index.sort_values()
tm.assert_index_equal(ordered, expected)
assert ordered.freq is None
ordered = index.sort_values(ascending=False)
tm.assert_index_equal(ordered, expected[::-1])
assert ordered.freq is None
ordered, indexer = index.sort_values(return_indexer=True)
tm.assert_index_equal(ordered, expected)
exp = np.array([0, 4, 3, 1, 2])
tm.assert_numpy_array_equal(indexer, exp, check_dtype=False)
assert ordered.freq is None
ordered, indexer = index.sort_values(return_indexer=True,
ascending=False)
tm.assert_index_equal(ordered, expected[::-1])
exp = np.array([2, 1, 3, 4, 0])
tm.assert_numpy_array_equal(indexer, exp, check_dtype=False)
assert ordered.freq is None
def test_drop_duplicates_metadata(self):
# GH 10115
idx = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx')
result = idx.drop_duplicates()
tm.assert_index_equal(idx, result)
assert idx.freq == result.freq
idx_dup = idx.append(idx)
assert idx_dup.freq is None # freq is reset
result = idx_dup.drop_duplicates()
tm.assert_index_equal(idx, result)
assert result.freq is None
def test_drop_duplicates(self):
# to check Index/Series compat
base = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx')
idx = base.append(base[:5])
res = idx.drop_duplicates()
tm.assert_index_equal(res, base)
res = Series(idx).drop_duplicates()
tm.assert_series_equal(res, Series(base))
res = idx.drop_duplicates(keep='last')
exp = base[5:].append(base[:5])
tm.assert_index_equal(res, exp)
res = Series(idx).drop_duplicates(keep='last')
tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36)))
res = idx.drop_duplicates(keep=False)
tm.assert_index_equal(res, base[5:])
res = Series(idx).drop_duplicates(keep=False)
tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31)))
@pytest.mark.parametrize('freq', [
'A', '2A', '-2A', 'Q', '-1Q', 'M', '-1M', 'D', '3D',
'-3D', 'W', '-1W', 'H', '2H', '-2H', 'T', '2T', 'S',
'-3S'])
def test_infer_freq(self, freq):
# GH 11018
idx = pd.date_range('2011-01-01 09:00:00', freq=freq, periods=10)
result = pd.DatetimeIndex(idx.asi8, freq='infer')
tm.assert_index_equal(idx, result)
assert result.freq == freq
def test_nat_new(self):
idx = pd.date_range('2011-01-01', freq='D', periods=5, name='x')
result = idx._nat_new()
exp = pd.DatetimeIndex([pd.NaT] * 5, name='x')
tm.assert_index_equal(result, exp)
result = idx._nat_new(box=False)
exp = np.array([tslib.iNaT] * 5, dtype=np.int64)
tm.assert_numpy_array_equal(result, exp)
def test_nat(self, tz_naive_fixture):
tz = tz_naive_fixture
assert pd.DatetimeIndex._na_value is pd.NaT
assert pd.DatetimeIndex([])._na_value is pd.NaT
idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02'], tz=tz)
assert idx._can_hold_na
tm.assert_numpy_array_equal(idx._isnan, np.array([False, False]))
assert idx.hasnans is False
tm.assert_numpy_array_equal(idx._nan_idxs,
np.array([], dtype=np.intp))
idx = pd.DatetimeIndex(['2011-01-01', 'NaT'], tz=tz)
assert idx._can_hold_na
tm.assert_numpy_array_equal(idx._isnan, np.array([False, True]))
assert idx.hasnans is True
tm.assert_numpy_array_equal(idx._nan_idxs,
np.array([1], dtype=np.intp))
def test_equals(self):
# GH 13107
idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02', 'NaT'])
assert idx.equals(idx)
assert idx.equals(idx.copy())
assert idx.equals(idx.astype(object))
assert idx.astype(object).equals(idx)
assert idx.astype(object).equals(idx.astype(object))
assert not idx.equals(list(idx))
assert not idx.equals(pd.Series(idx))
idx2 = pd.DatetimeIndex(['2011-01-01', '2011-01-02', 'NaT'],
tz='US/Pacific')
assert not idx.equals(idx2)
assert not idx.equals(idx2.copy())
assert not idx.equals(idx2.astype(object))
assert not idx.astype(object).equals(idx2)
assert not idx.equals(list(idx2))
assert not idx.equals(pd.Series(idx2))
# same internal, different tz
idx3 = pd.DatetimeIndex._simple_new(idx.asi8, tz='US/Pacific')
tm.assert_numpy_array_equal(idx.asi8, idx3.asi8)
assert not idx.equals(idx3)
assert not idx.equals(idx3.copy())
assert not idx.equals(idx3.astype(object))
assert not idx.astype(object).equals(idx3)
assert not idx.equals(list(idx3))
assert not idx.equals(pd.Series(idx3))
@pytest.mark.parametrize('values', [
['20180101', '20180103', '20180105'], []])
@pytest.mark.parametrize('freq', [
'2D', Day(2), '2B', BDay(2), '48H', Hour(48)])
@pytest.mark.parametrize('tz', [None, 'US/Eastern'])
def test_freq_setter(self, values, freq, tz):
# GH 20678
idx = DatetimeIndex(values, tz=tz)
# can set to an offset, converting from string if necessary
idx.freq = freq
assert idx.freq == freq
assert isinstance(idx.freq, ABCDateOffset)
# can reset to None
idx.freq = None
assert idx.freq is None
def test_freq_setter_errors(self):
# GH 20678
idx = DatetimeIndex(['20180101', '20180103', '20180105'])
# setting with an incompatible freq
msg = ('Inferred frequency 2D from passed values does not conform to '
'passed frequency 5D')
with tm.assert_raises_regex(ValueError, msg):
idx.freq = '5D'
# setting with non-freq string
with tm.assert_raises_regex(ValueError, 'Invalid frequency'):
idx.freq = 'foo'
def test_offset_deprecated(self):
# GH 20716
idx = pd.DatetimeIndex(['20180101', '20180102'])
# getter deprecated
with tm.assert_produces_warning(FutureWarning):
idx.offset
# setter deprecated
with tm.assert_produces_warning(FutureWarning):
idx.offset = BDay()
class TestBusinessDatetimeIndex(object):
def setup_method(self, method):
self.rng = bdate_range(START, END)
def test_comparison(self):
d = self.rng[10]
comp = self.rng > d
assert comp[11]
assert not comp[9]
def test_pickle_unpickle(self):
unpickled = tm.round_trip_pickle(self.rng)
assert unpickled.freq is not None
def test_copy(self):
cp = self.rng.copy()
repr(cp)
tm.assert_index_equal(cp, self.rng)
def test_shift(self):
shifted = self.rng.shift(5)
assert shifted[0] == self.rng[5]
assert shifted.freq == self.rng.freq
shifted = self.rng.shift(-5)
assert shifted[5] == self.rng[0]
assert shifted.freq == self.rng.freq
shifted = self.rng.shift(0)
assert shifted[0] == self.rng[0]
assert shifted.freq == self.rng.freq
rng = date_range(START, END, freq=BMonthEnd())
shifted = rng.shift(1, freq=BDay())
assert shifted[0] == rng[0] + BDay()
def test_equals(self):
assert not self.rng.equals(list(self.rng))
def test_identical(self):
t1 = self.rng.copy()
t2 = self.rng.copy()
assert t1.identical(t2)
# name
t1 = t1.rename('foo')
assert t1.equals(t2)
assert not t1.identical(t2)
t2 = t2.rename('foo')
assert t1.identical(t2)
# freq
t2v = Index(t2.values)
assert t1.equals(t2v)
assert not t1.identical(t2v)
class TestCustomDatetimeIndex(object):
def setup_method(self, method):
self.rng = bdate_range(START, END, freq='C')
def test_comparison(self):
d = self.rng[10]
comp = self.rng > d
assert comp[11]
assert not comp[9]
def test_copy(self):
cp = self.rng.copy()
repr(cp)
tm.assert_index_equal(cp, self.rng)
def test_shift(self):
shifted = self.rng.shift(5)
assert shifted[0] == self.rng[5]
assert shifted.freq == self.rng.freq
shifted = self.rng.shift(-5)
assert shifted[5] == self.rng[0]
assert shifted.freq == self.rng.freq
shifted = self.rng.shift(0)
assert shifted[0] == self.rng[0]
assert shifted.freq == self.rng.freq
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", pd.errors.PerformanceWarning)
rng = date_range(START, END, freq=BMonthEnd())
shifted = rng.shift(1, freq=CDay())
assert shifted[0] == rng[0] + CDay()
def test_shift_periods(self):
# GH #22458 : argument 'n' was deprecated in favor of 'periods'
idx = pd.DatetimeIndex(start=START, end=END,
periods=3)
tm.assert_index_equal(idx.shift(periods=0), idx)
tm.assert_index_equal(idx.shift(0), idx)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=True):
tm.assert_index_equal(idx.shift(n=0), idx)
def test_pickle_unpickle(self):
unpickled = tm.round_trip_pickle(self.rng)
assert unpickled.freq is not None
def test_equals(self):
assert not self.rng.equals(list(self.rng))