laywerrobot/lib/python3.6/site-packages/pandas/tests/series/indexing/test_datetime.py
2020-08-27 21:55:39 +02:00

709 lines
20 KiB
Python

# coding=utf-8
# pylint: disable-msg=E1101,W0612
import pytest
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, DataFrame,
date_range, Timestamp, DatetimeIndex, NaT)
from pandas.compat import lrange, range
from pandas.util.testing import (assert_series_equal,
assert_frame_equal, assert_almost_equal)
import pandas.util.testing as tm
import pandas._libs.index as _index
from pandas._libs import tslib
"""
Also test support for datetime64[ns] in Series / DataFrame
"""
def test_fancy_getitem():
dti = DatetimeIndex(freq='WOM-1FRI', start=datetime(2005, 1, 1),
end=datetime(2010, 1, 1))
s = Series(np.arange(len(dti)), index=dti)
assert s[48] == 48
assert s['1/2/2009'] == 48
assert s['2009-1-2'] == 48
assert s[datetime(2009, 1, 2)] == 48
assert s[Timestamp(datetime(2009, 1, 2))] == 48
pytest.raises(KeyError, s.__getitem__, '2009-1-3')
assert_series_equal(s['3/6/2009':'2009-06-05'],
s[datetime(2009, 3, 6):datetime(2009, 6, 5)])
def test_fancy_setitem():
dti = DatetimeIndex(freq='WOM-1FRI', start=datetime(2005, 1, 1),
end=datetime(2010, 1, 1))
s = Series(np.arange(len(dti)), index=dti)
s[48] = -1
assert s[48] == -1
s['1/2/2009'] = -2
assert s[48] == -2
s['1/2/2009':'2009-06-05'] = -3
assert (s[48:54] == -3).all()
def test_dti_snap():
dti = DatetimeIndex(['1/1/2002', '1/2/2002', '1/3/2002', '1/4/2002',
'1/5/2002', '1/6/2002', '1/7/2002'], freq='D')
res = dti.snap(freq='W-MON')
exp = date_range('12/31/2001', '1/7/2002', freq='w-mon')
exp = exp.repeat([3, 4])
assert (res == exp).all()
res = dti.snap(freq='B')
exp = date_range('1/1/2002', '1/7/2002', freq='b')
exp = exp.repeat([1, 1, 1, 2, 2])
assert (res == exp).all()
def test_dti_reset_index_round_trip():
dti = DatetimeIndex(start='1/1/2001', end='6/1/2001', freq='D')
d1 = DataFrame({'v': np.random.rand(len(dti))}, index=dti)
d2 = d1.reset_index()
assert d2.dtypes[0] == np.dtype('M8[ns]')
d3 = d2.set_index('index')
assert_frame_equal(d1, d3, check_names=False)
# #2329
stamp = datetime(2012, 11, 22)
df = DataFrame([[stamp, 12.1]], columns=['Date', 'Value'])
df = df.set_index('Date')
assert df.index[0] == stamp
assert df.reset_index()['Date'][0] == stamp
def test_series_set_value():
# #1561
dates = [datetime(2001, 1, 1), datetime(2001, 1, 2)]
index = DatetimeIndex(dates)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
s = Series().set_value(dates[0], 1.)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
s2 = s.set_value(dates[1], np.nan)
exp = Series([1., np.nan], index=index)
assert_series_equal(s2, exp)
# s = Series(index[:1], index[:1])
# s2 = s.set_value(dates[1], index[1])
# assert s2.values.dtype == 'M8[ns]'
@pytest.mark.slow
def test_slice_locs_indexerror():
times = [datetime(2000, 1, 1) + timedelta(minutes=i * 10)
for i in range(100000)]
s = Series(lrange(100000), times)
s.loc[datetime(1900, 1, 1):datetime(2100, 1, 1)]
def test_slicing_datetimes():
# GH 7523
# unique
df = DataFrame(np.arange(4., dtype='float64'),
index=[datetime(2001, 1, i, 10, 00)
for i in [1, 2, 3, 4]])
result = df.loc[datetime(2001, 1, 1, 10):]
assert_frame_equal(result, df)
result = df.loc[:datetime(2001, 1, 4, 10)]
assert_frame_equal(result, df)
result = df.loc[datetime(2001, 1, 1, 10):datetime(2001, 1, 4, 10)]
assert_frame_equal(result, df)
result = df.loc[datetime(2001, 1, 1, 11):]
expected = df.iloc[1:]
assert_frame_equal(result, expected)
result = df.loc['20010101 11':]
assert_frame_equal(result, expected)
# duplicates
df = pd.DataFrame(np.arange(5., dtype='float64'),
index=[datetime(2001, 1, i, 10, 00)
for i in [1, 2, 2, 3, 4]])
result = df.loc[datetime(2001, 1, 1, 10):]
assert_frame_equal(result, df)
result = df.loc[:datetime(2001, 1, 4, 10)]
assert_frame_equal(result, df)
result = df.loc[datetime(2001, 1, 1, 10):datetime(2001, 1, 4, 10)]
assert_frame_equal(result, df)
result = df.loc[datetime(2001, 1, 1, 11):]
expected = df.iloc[1:]
assert_frame_equal(result, expected)
result = df.loc['20010101 11':]
assert_frame_equal(result, expected)
def test_frame_datetime64_duplicated():
dates = date_range('2010-07-01', end='2010-08-05')
tst = DataFrame({'symbol': 'AAA', 'date': dates})
result = tst.duplicated(['date', 'symbol'])
assert (-result).all()
tst = DataFrame({'date': dates})
result = tst.duplicated()
assert (-result).all()
def test_getitem_setitem_datetime_tz_pytz():
from pytz import timezone as tz
from pandas import date_range
N = 50
# testing with timezone, GH #2785
rng = date_range('1/1/1990', periods=N, freq='H', tz='US/Eastern')
ts = Series(np.random.randn(N), index=rng)
# also test Timestamp tz handling, GH #2789
result = ts.copy()
result["1990-01-01 09:00:00+00:00"] = 0
result["1990-01-01 09:00:00+00:00"] = ts[4]
assert_series_equal(result, ts)
result = ts.copy()
result["1990-01-01 03:00:00-06:00"] = 0
result["1990-01-01 03:00:00-06:00"] = ts[4]
assert_series_equal(result, ts)
# repeat with datetimes
result = ts.copy()
result[datetime(1990, 1, 1, 9, tzinfo=tz('UTC'))] = 0
result[datetime(1990, 1, 1, 9, tzinfo=tz('UTC'))] = ts[4]
assert_series_equal(result, ts)
result = ts.copy()
# comparison dates with datetime MUST be localized!
date = tz('US/Central').localize(datetime(1990, 1, 1, 3))
result[date] = 0
result[date] = ts[4]
assert_series_equal(result, ts)
def test_getitem_setitem_datetime_tz_dateutil():
from dateutil.tz import tzutc
from pandas._libs.tslibs.timezones import dateutil_gettz as gettz
tz = lambda x: tzutc() if x == 'UTC' else gettz(
x) # handle special case for utc in dateutil
from pandas import date_range
N = 50
# testing with timezone, GH #2785
rng = date_range('1/1/1990', periods=N, freq='H',
tz='America/New_York')
ts = Series(np.random.randn(N), index=rng)
# also test Timestamp tz handling, GH #2789
result = ts.copy()
result["1990-01-01 09:00:00+00:00"] = 0
result["1990-01-01 09:00:00+00:00"] = ts[4]
assert_series_equal(result, ts)
result = ts.copy()
result["1990-01-01 03:00:00-06:00"] = 0
result["1990-01-01 03:00:00-06:00"] = ts[4]
assert_series_equal(result, ts)
# repeat with datetimes
result = ts.copy()
result[datetime(1990, 1, 1, 9, tzinfo=tz('UTC'))] = 0
result[datetime(1990, 1, 1, 9, tzinfo=tz('UTC'))] = ts[4]
assert_series_equal(result, ts)
result = ts.copy()
result[datetime(1990, 1, 1, 3, tzinfo=tz('America/Chicago'))] = 0
result[datetime(1990, 1, 1, 3, tzinfo=tz('America/Chicago'))] = ts[4]
assert_series_equal(result, ts)
def test_getitem_setitem_datetimeindex():
N = 50
# testing with timezone, GH #2785
rng = date_range('1/1/1990', periods=N, freq='H', tz='US/Eastern')
ts = Series(np.random.randn(N), index=rng)
result = ts["1990-01-01 04:00:00"]
expected = ts[4]
assert result == expected
result = ts.copy()
result["1990-01-01 04:00:00"] = 0
result["1990-01-01 04:00:00"] = ts[4]
assert_series_equal(result, ts)
result = ts["1990-01-01 04:00:00":"1990-01-01 07:00:00"]
expected = ts[4:8]
assert_series_equal(result, expected)
result = ts.copy()
result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = 0
result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = ts[4:8]
assert_series_equal(result, ts)
lb = "1990-01-01 04:00:00"
rb = "1990-01-01 07:00:00"
# GH#18435 strings get a pass from tzawareness compat
result = ts[(ts.index >= lb) & (ts.index <= rb)]
expected = ts[4:8]
assert_series_equal(result, expected)
lb = "1990-01-01 04:00:00-0500"
rb = "1990-01-01 07:00:00-0500"
result = ts[(ts.index >= lb) & (ts.index <= rb)]
expected = ts[4:8]
assert_series_equal(result, expected)
# repeat all the above with naive datetimes
result = ts[datetime(1990, 1, 1, 4)]
expected = ts[4]
assert result == expected
result = ts.copy()
result[datetime(1990, 1, 1, 4)] = 0
result[datetime(1990, 1, 1, 4)] = ts[4]
assert_series_equal(result, ts)
result = ts[datetime(1990, 1, 1, 4):datetime(1990, 1, 1, 7)]
expected = ts[4:8]
assert_series_equal(result, expected)
result = ts.copy()
result[datetime(1990, 1, 1, 4):datetime(1990, 1, 1, 7)] = 0
result[datetime(1990, 1, 1, 4):datetime(1990, 1, 1, 7)] = ts[4:8]
assert_series_equal(result, ts)
lb = datetime(1990, 1, 1, 4)
rb = datetime(1990, 1, 1, 7)
with pytest.raises(TypeError):
# tznaive vs tzaware comparison is invalid
# see GH#18376, GH#18162
ts[(ts.index >= lb) & (ts.index <= rb)]
lb = pd.Timestamp(datetime(1990, 1, 1, 4)).tz_localize(rng.tzinfo)
rb = pd.Timestamp(datetime(1990, 1, 1, 7)).tz_localize(rng.tzinfo)
result = ts[(ts.index >= lb) & (ts.index <= rb)]
expected = ts[4:8]
assert_series_equal(result, expected)
result = ts[ts.index[4]]
expected = ts[4]
assert result == expected
result = ts[ts.index[4:8]]
expected = ts[4:8]
assert_series_equal(result, expected)
result = ts.copy()
result[ts.index[4:8]] = 0
result[4:8] = ts[4:8]
assert_series_equal(result, ts)
# also test partial date slicing
result = ts["1990-01-02"]
expected = ts[24:48]
assert_series_equal(result, expected)
result = ts.copy()
result["1990-01-02"] = 0
result["1990-01-02"] = ts[24:48]
assert_series_equal(result, ts)
def test_getitem_setitem_periodindex():
from pandas import period_range
N = 50
rng = period_range('1/1/1990', periods=N, freq='H')
ts = Series(np.random.randn(N), index=rng)
result = ts["1990-01-01 04"]
expected = ts[4]
assert result == expected
result = ts.copy()
result["1990-01-01 04"] = 0
result["1990-01-01 04"] = ts[4]
assert_series_equal(result, ts)
result = ts["1990-01-01 04":"1990-01-01 07"]
expected = ts[4:8]
assert_series_equal(result, expected)
result = ts.copy()
result["1990-01-01 04":"1990-01-01 07"] = 0
result["1990-01-01 04":"1990-01-01 07"] = ts[4:8]
assert_series_equal(result, ts)
lb = "1990-01-01 04"
rb = "1990-01-01 07"
result = ts[(ts.index >= lb) & (ts.index <= rb)]
expected = ts[4:8]
assert_series_equal(result, expected)
# GH 2782
result = ts[ts.index[4]]
expected = ts[4]
assert result == expected
result = ts[ts.index[4:8]]
expected = ts[4:8]
assert_series_equal(result, expected)
result = ts.copy()
result[ts.index[4:8]] = 0
result[4:8] = ts[4:8]
assert_series_equal(result, ts)
def test_getitem_median_slice_bug():
index = date_range('20090415', '20090519', freq='2B')
s = Series(np.random.randn(13), index=index)
indexer = [slice(6, 7, None)]
result = s[indexer]
expected = s[indexer[0]]
assert_series_equal(result, expected)
def test_datetime_indexing():
from pandas import date_range
index = date_range('1/1/2000', '1/7/2000')
index = index.repeat(3)
s = Series(len(index), index=index)
stamp = Timestamp('1/8/2000')
pytest.raises(KeyError, s.__getitem__, stamp)
s[stamp] = 0
assert s[stamp] == 0
# not monotonic
s = Series(len(index), index=index)
s = s[::-1]
pytest.raises(KeyError, s.__getitem__, stamp)
s[stamp] = 0
assert s[stamp] == 0
"""
test duplicates in time series
"""
@pytest.fixture(scope='module')
def dups():
dates = [datetime(2000, 1, 2), datetime(2000, 1, 2),
datetime(2000, 1, 2), datetime(2000, 1, 3),
datetime(2000, 1, 3), datetime(2000, 1, 3),
datetime(2000, 1, 4), datetime(2000, 1, 4),
datetime(2000, 1, 4), datetime(2000, 1, 5)]
return Series(np.random.randn(len(dates)), index=dates)
def test_constructor(dups):
assert isinstance(dups, Series)
assert isinstance(dups.index, DatetimeIndex)
def test_is_unique_monotonic(dups):
assert not dups.index.is_unique
def test_index_unique(dups):
uniques = dups.index.unique()
expected = DatetimeIndex([datetime(2000, 1, 2), datetime(2000, 1, 3),
datetime(2000, 1, 4), datetime(2000, 1, 5)])
assert uniques.dtype == 'M8[ns]' # sanity
tm.assert_index_equal(uniques, expected)
assert dups.index.nunique() == 4
# #2563
assert isinstance(uniques, DatetimeIndex)
dups_local = dups.index.tz_localize('US/Eastern')
dups_local.name = 'foo'
result = dups_local.unique()
expected = DatetimeIndex(expected, name='foo')
expected = expected.tz_localize('US/Eastern')
assert result.tz is not None
assert result.name == 'foo'
tm.assert_index_equal(result, expected)
# NaT, note this is excluded
arr = [1370745748 + t for t in range(20)] + [tslib.iNaT]
idx = DatetimeIndex(arr * 3)
tm.assert_index_equal(idx.unique(), DatetimeIndex(arr))
assert idx.nunique() == 20
assert idx.nunique(dropna=False) == 21
arr = [Timestamp('2013-06-09 02:42:28') + timedelta(seconds=t)
for t in range(20)] + [NaT]
idx = DatetimeIndex(arr * 3)
tm.assert_index_equal(idx.unique(), DatetimeIndex(arr))
assert idx.nunique() == 20
assert idx.nunique(dropna=False) == 21
def test_index_dupes_contains():
d = datetime(2011, 12, 5, 20, 30)
ix = DatetimeIndex([d, d])
assert d in ix
def test_duplicate_dates_indexing(dups):
ts = dups
uniques = ts.index.unique()
for date in uniques:
result = ts[date]
mask = ts.index == date
total = (ts.index == date).sum()
expected = ts[mask]
if total > 1:
assert_series_equal(result, expected)
else:
assert_almost_equal(result, expected[0])
cp = ts.copy()
cp[date] = 0
expected = Series(np.where(mask, 0, ts), index=ts.index)
assert_series_equal(cp, expected)
pytest.raises(KeyError, ts.__getitem__, datetime(2000, 1, 6))
# new index
ts[datetime(2000, 1, 6)] = 0
assert ts[datetime(2000, 1, 6)] == 0
def test_range_slice():
idx = DatetimeIndex(['1/1/2000', '1/2/2000', '1/2/2000', '1/3/2000',
'1/4/2000'])
ts = Series(np.random.randn(len(idx)), index=idx)
result = ts['1/2/2000':]
expected = ts[1:]
assert_series_equal(result, expected)
result = ts['1/2/2000':'1/3/2000']
expected = ts[1:4]
assert_series_equal(result, expected)
def test_groupby_average_dup_values(dups):
result = dups.groupby(level=0).mean()
expected = dups.groupby(dups.index).mean()
assert_series_equal(result, expected)
def test_indexing_over_size_cutoff():
import datetime
# #1821
old_cutoff = _index._SIZE_CUTOFF
try:
_index._SIZE_CUTOFF = 1000
# create large list of non periodic datetime
dates = []
sec = datetime.timedelta(seconds=1)
half_sec = datetime.timedelta(microseconds=500000)
d = datetime.datetime(2011, 12, 5, 20, 30)
n = 1100
for i in range(n):
dates.append(d)
dates.append(d + sec)
dates.append(d + sec + half_sec)
dates.append(d + sec + sec + half_sec)
d += 3 * sec
# duplicate some values in the list
duplicate_positions = np.random.randint(0, len(dates) - 1, 20)
for p in duplicate_positions:
dates[p + 1] = dates[p]
df = DataFrame(np.random.randn(len(dates), 4),
index=dates,
columns=list('ABCD'))
pos = n * 3
timestamp = df.index[pos]
assert timestamp in df.index
# it works!
df.loc[timestamp]
assert len(df.loc[[timestamp]]) > 0
finally:
_index._SIZE_CUTOFF = old_cutoff
def test_indexing_unordered():
# GH 2437
rng = date_range(start='2011-01-01', end='2011-01-15')
ts = Series(np.random.rand(len(rng)), index=rng)
ts2 = pd.concat([ts[0:4], ts[-4:], ts[4:-4]])
for t in ts.index:
# TODO: unused?
s = str(t) # noqa
expected = ts[t]
result = ts2[t]
assert expected == result
# GH 3448 (ranges)
def compare(slobj):
result = ts2[slobj].copy()
result = result.sort_index()
expected = ts[slobj]
assert_series_equal(result, expected)
compare(slice('2011-01-01', '2011-01-15'))
compare(slice('2010-12-30', '2011-01-15'))
compare(slice('2011-01-01', '2011-01-16'))
# partial ranges
compare(slice('2011-01-01', '2011-01-6'))
compare(slice('2011-01-06', '2011-01-8'))
compare(slice('2011-01-06', '2011-01-12'))
# single values
result = ts2['2011'].sort_index()
expected = ts['2011']
assert_series_equal(result, expected)
# diff freq
rng = date_range(datetime(2005, 1, 1), periods=20, freq='M')
ts = Series(np.arange(len(rng)), index=rng)
ts = ts.take(np.random.permutation(20))
result = ts['2005']
for t in result.index:
assert t.year == 2005
def test_indexing():
idx = date_range("2001-1-1", periods=20, freq='M')
ts = Series(np.random.rand(len(idx)), index=idx)
# getting
# GH 3070, make sure semantics work on Series/Frame
expected = ts['2001']
expected.name = 'A'
df = DataFrame(dict(A=ts))
result = df['2001']['A']
assert_series_equal(expected, result)
# setting
ts['2001'] = 1
expected = ts['2001']
expected.name = 'A'
df.loc['2001', 'A'] = 1
result = df['2001']['A']
assert_series_equal(expected, result)
# GH3546 (not including times on the last day)
idx = date_range(start='2013-05-31 00:00', end='2013-05-31 23:00',
freq='H')
ts = Series(lrange(len(idx)), index=idx)
expected = ts['2013-05']
assert_series_equal(expected, ts)
idx = date_range(start='2013-05-31 00:00', end='2013-05-31 23:59',
freq='S')
ts = Series(lrange(len(idx)), index=idx)
expected = ts['2013-05']
assert_series_equal(expected, ts)
idx = [Timestamp('2013-05-31 00:00'),
Timestamp(datetime(2013, 5, 31, 23, 59, 59, 999999))]
ts = Series(lrange(len(idx)), index=idx)
expected = ts['2013']
assert_series_equal(expected, ts)
# GH14826, indexing with a seconds resolution string / datetime object
df = DataFrame(np.random.rand(5, 5),
columns=['open', 'high', 'low', 'close', 'volume'],
index=date_range('2012-01-02 18:01:00',
periods=5, tz='US/Central', freq='s'))
expected = df.loc[[df.index[2]]]
# this is a single date, so will raise
pytest.raises(KeyError, df.__getitem__, '2012-01-02 18:01:02', )
pytest.raises(KeyError, df.__getitem__, df.index[2], )
"""
test NaT support
"""
def test_set_none_nan():
series = Series(date_range('1/1/2000', periods=10))
series[3] = None
assert series[3] is NaT
series[3:5] = None
assert series[4] is NaT
series[5] = np.nan
assert series[5] is NaT
series[5:7] = np.nan
assert series[6] is NaT
def test_nat_operations():
# GH 8617
s = Series([0, pd.NaT], dtype='m8[ns]')
exp = s[0]
assert s.median() == exp
assert s.min() == exp
assert s.max() == exp
@pytest.mark.parametrize('method', ["round", "floor", "ceil"])
@pytest.mark.parametrize('freq', ["s", "5s", "min", "5min", "h", "5h"])
def test_round_nat(method, freq):
# GH14940
s = Series([pd.NaT])
expected = Series(pd.NaT)
round_method = getattr(s.dt, method)
assert_series_equal(round_method(freq), expected)