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test_datetime.py
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from datetime import date
import dateutil
import numpy as np
import pytest
from pandas.compat import lrange
import pandas as pd
from pandas import (
DataFrame, DatetimeIndex, Index, Timestamp, date_range, offsets)
import pandas.util.testing as tm
from pandas.util.testing import assert_almost_equal
randn = np.random.randn
class TestDatetimeIndex(object):
def test_roundtrip_pickle_with_tz(self):
# GH 8367
# round-trip of timezone
index = date_range('20130101', periods=3, tz='US/Eastern', name='foo')
unpickled = tm.round_trip_pickle(index)
tm.assert_index_equal(index, unpickled)
def test_reindex_preserves_tz_if_target_is_empty_list_or_array(self):
# GH7774
index = date_range('20130101', periods=3, tz='US/Eastern')
assert str(index.reindex([])[0].tz) == 'US/Eastern'
assert str(index.reindex(np.array([]))[0].tz) == 'US/Eastern'
def test_time_loc(self): # GH8667
from datetime import time
from pandas._libs.index import _SIZE_CUTOFF
ns = _SIZE_CUTOFF + np.array([-100, 100], dtype=np.int64)
key = time(15, 11, 30)
start = key.hour * 3600 + key.minute * 60 + key.second
step = 24 * 3600
for n in ns:
idx = pd.date_range('2014-11-26', periods=n, freq='S')
ts = pd.Series(np.random.randn(n), index=idx)
i = np.arange(start, n, step)
tm.assert_numpy_array_equal(ts.index.get_loc(key), i,
check_dtype=False)
tm.assert_series_equal(ts[key], ts.iloc[i])
left, right = ts.copy(), ts.copy()
left[key] *= -10
right.iloc[i] *= -10
tm.assert_series_equal(left, right)
def test_time_overflow_for_32bit_machines(self):
# GH8943. On some machines NumPy defaults to np.int32 (for example,
# 32-bit Linux machines). In the function _generate_regular_range
# found in tseries/index.py, `periods` gets multiplied by `strides`
# (which has value 1e9) and since the max value for np.int32 is ~2e9,
# and since those machines won't promote np.int32 to np.int64, we get
# overflow.
periods = np.int_(1000)
idx1 = pd.date_range(start='2000', periods=periods, freq='S')
assert len(idx1) == periods
idx2 = pd.date_range(end='2000', periods=periods, freq='S')
assert len(idx2) == periods
def test_nat(self):
assert DatetimeIndex([np.nan])[0] is pd.NaT
def test_week_of_month_frequency(self):
# GH 5348: "ValueError: Could not evaluate WOM-1SUN" shouldn't raise
d1 = date(2002, 9, 1)
d2 = date(2013, 10, 27)
d3 = date(2012, 9, 30)
idx1 = DatetimeIndex([d1, d2])
idx2 = DatetimeIndex([d3])
result_append = idx1.append(idx2)
expected = DatetimeIndex([d1, d2, d3])
tm.assert_index_equal(result_append, expected)
result_union = idx1.union(idx2)
expected = DatetimeIndex([d1, d3, d2])
tm.assert_index_equal(result_union, expected)
# GH 5115
result = date_range("2013-1-1", periods=4, freq='WOM-1SAT')
dates = ['2013-01-05', '2013-02-02', '2013-03-02', '2013-04-06']
expected = DatetimeIndex(dates, freq='WOM-1SAT')
tm.assert_index_equal(result, expected)
def test_hash_error(self):
index = date_range('20010101', periods=10)
with pytest.raises(TypeError, match=("unhashable type: %r" %
type(index).__name__)):
hash(index)
def test_stringified_slice_with_tz(self):
# GH#2658
start = '2013-01-07'
idx = date_range(start=start, freq="1d", periods=10, tz='US/Eastern')
df = DataFrame(lrange(10), index=idx)
df["2013-01-14 23:44:34.437768-05:00":] # no exception here
def test_append_join_nondatetimeindex(self):
rng = date_range('1/1/2000', periods=10)
idx = Index(['a', 'b', 'c', 'd'])
result = rng.append(idx)
assert isinstance(result[0], Timestamp)
# it works
rng.join(idx, how='outer')
def test_map(self):
rng = date_range('1/1/2000', periods=10)
f = lambda x: x.strftime('%Y%m%d')
result = rng.map(f)
exp = Index([f(x) for x in rng], dtype='<U8')
tm.assert_index_equal(result, exp)
def test_map_fallthrough(self, capsys):
# GH#22067, check we don't get warnings about silently ignored errors
dti = date_range('2017-01-01', '2018-01-01', freq='B')
dti.map(lambda x: pd.Period(year=x.year, month=x.month, freq='M'))
captured = capsys.readouterr()
assert captured.err == ''
def test_iteration_preserves_tz(self):
# see gh-8890
index = date_range("2012-01-01", periods=3, freq='H', tz='US/Eastern')
for i, ts in enumerate(index):
result = ts
expected = index[i]
assert result == expected
index = date_range("2012-01-01", periods=3, freq='H',
tz=dateutil.tz.tzoffset(None, -28800))
for i, ts in enumerate(index):
result = ts
expected = index[i]
assert result._repr_base == expected._repr_base
assert result == expected
# 9100
index = pd.DatetimeIndex(['2014-12-01 03:32:39.987000-08:00',
'2014-12-01 04:12:34.987000-08:00'])
for i, ts in enumerate(index):
result = ts
expected = index[i]
assert result._repr_base == expected._repr_base
assert result == expected
@pytest.mark.parametrize('periods', [0, 9999, 10000, 10001])
def test_iteration_over_chunksize(self, periods):
# GH21012
index = date_range('2000-01-01 00:00:00', periods=periods, freq='min')
num = 0
for stamp in index:
assert index[num] == stamp
num += 1
assert num == len(index)
def test_misc_coverage(self):
rng = date_range('1/1/2000', periods=5)
result = rng.groupby(rng.day)
assert isinstance(list(result.values())[0][0], Timestamp)
idx = DatetimeIndex(['2000-01-03', '2000-01-01', '2000-01-02'])
assert not idx.equals(list(idx))
non_datetime = Index(list('abc'))
assert not idx.equals(list(non_datetime))
def test_string_index_series_name_converted(self):
# #1644
df = DataFrame(np.random.randn(10, 4),
index=date_range('1/1/2000', periods=10))
result = df.loc['1/3/2000']
assert result.name == df.index[2]
result = df.T['1/3/2000']
assert result.name == df.index[2]
def test_get_duplicates(self):
idx = DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-02',
'2000-01-03', '2000-01-03', '2000-01-04'])
with tm.assert_produces_warning(FutureWarning):
# Deprecated - see GH20239
result = idx.get_duplicates()
ex = DatetimeIndex(['2000-01-02', '2000-01-03'])
tm.assert_index_equal(result, ex)
def test_argmin_argmax(self):
idx = DatetimeIndex(['2000-01-04', '2000-01-01', '2000-01-02'])
assert idx.argmin() == 1
assert idx.argmax() == 0
def test_sort_values(self):
idx = DatetimeIndex(['2000-01-04', '2000-01-01', '2000-01-02'])
ordered = idx.sort_values()
assert ordered.is_monotonic
ordered = idx.sort_values(ascending=False)
assert ordered[::-1].is_monotonic
ordered, dexer = idx.sort_values(return_indexer=True)
assert ordered.is_monotonic
tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0], dtype=np.intp))
ordered, dexer = idx.sort_values(return_indexer=True, ascending=False)
assert ordered[::-1].is_monotonic
tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1], dtype=np.intp))
def test_map_bug_1677(self):
index = DatetimeIndex(['2012-04-25 09:30:00.393000'])
f = index.asof
result = index.map(f)
expected = Index([f(index[0])])
tm.assert_index_equal(result, expected)
def test_groupby_function_tuple_1677(self):
df = DataFrame(np.random.rand(100),
index=date_range("1/1/2000", periods=100))
monthly_group = df.groupby(lambda x: (x.year, x.month))
result = monthly_group.mean()
assert isinstance(result.index[0], tuple)
def test_append_numpy_bug_1681(self):
# another datetime64 bug
dr = date_range('2011/1/1', '2012/1/1', freq='W-FRI')
a = DataFrame()
c = DataFrame({'A': 'foo', 'B': dr}, index=dr)
result = a.append(c)
assert (result['B'] == dr).all()
def test_isin(self):
index = tm.makeDateIndex(4)
result = index.isin(index)
assert result.all()
result = index.isin(list(index))
assert result.all()
assert_almost_equal(index.isin([index[2], 5]),
np.array([False, False, True, False]))
def test_does_not_convert_mixed_integer(self):
df = tm.makeCustomDataframe(10, 10,
data_gen_f=lambda *args, **kwargs: randn(),
r_idx_type='i', c_idx_type='dt')
cols = df.columns.join(df.index, how='outer')
joined = cols.join(df.columns)
assert cols.dtype == np.dtype('O')
assert cols.dtype == joined.dtype
tm.assert_numpy_array_equal(cols.values, joined.values)
def test_join_self(self, join_type):
index = date_range('1/1/2000', periods=10)
joined = index.join(index, how=join_type)
assert index is joined
def assert_index_parameters(self, index):
assert index.freq == '40960N'
assert index.inferred_freq == '40960N'
def test_ns_index(self):
nsamples = 400
ns = int(1e9 / 24414)
dtstart = np.datetime64('2012-09-20T00:00:00')
dt = dtstart + np.arange(nsamples) * np.timedelta64(ns, 'ns')
freq = ns * offsets.Nano()
index = pd.DatetimeIndex(dt, freq=freq, name='time')
self.assert_index_parameters(index)
new_index = pd.date_range(start=index[0], end=index[-1],
freq=index.freq)
self.assert_index_parameters(new_index)
def test_join_with_period_index(self, join_type):
df = tm.makeCustomDataframe(
10, 10, data_gen_f=lambda *args: np.random.randint(2),
c_idx_type='p', r_idx_type='dt')
s = df.iloc[:5, 0]
msg = 'can only call with other PeriodIndex-ed objects'
with pytest.raises(ValueError, match=msg):
df.columns.join(s.index, how=join_type)
def test_factorize(self):
idx1 = DatetimeIndex(['2014-01', '2014-01', '2014-02', '2014-02',
'2014-03', '2014-03'])
exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
exp_idx = DatetimeIndex(['2014-01', '2014-02', '2014-03'])
arr, idx = idx1.factorize()
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, exp_idx)
arr, idx = idx1.factorize(sort=True)
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, exp_idx)
# tz must be preserved
idx1 = idx1.tz_localize('Asia/Tokyo')
exp_idx = exp_idx.tz_localize('Asia/Tokyo')
arr, idx = idx1.factorize()
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, exp_idx)
idx2 = pd.DatetimeIndex(['2014-03', '2014-03', '2014-02', '2014-01',
'2014-03', '2014-01'])
exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.intp)
exp_idx = DatetimeIndex(['2014-01', '2014-02', '2014-03'])
arr, idx = idx2.factorize(sort=True)
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, exp_idx)
exp_arr = np.array([0, 0, 1, 2, 0, 2], dtype=np.intp)
exp_idx = DatetimeIndex(['2014-03', '2014-02', '2014-01'])
arr, idx = idx2.factorize()
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, exp_idx)
# freq must be preserved
idx3 = date_range('2000-01', periods=4, freq='M', tz='Asia/Tokyo')
exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
arr, idx = idx3.factorize()
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, idx3)
def test_factorize_tz(self, tz_naive_fixture):
tz = tz_naive_fixture
# GH#13750
base = pd.date_range('2016-11-05', freq='H', periods=100, tz=tz)
idx = base.repeat(5)
exp_arr = np.arange(100, dtype=np.intp).repeat(5)
for obj in [idx, pd.Series(idx)]:
arr, res = obj.factorize()
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(res, base)
def test_factorize_dst(self):
# GH 13750
idx = pd.date_range('2016-11-06', freq='H', periods=12,
tz='US/Eastern')
for obj in [idx, pd.Series(idx)]:
arr, res = obj.factorize()
tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
tm.assert_index_equal(res, idx)
idx = pd.date_range('2016-06-13', freq='H', periods=12,
tz='US/Eastern')
for obj in [idx, pd.Series(idx)]:
arr, res = obj.factorize()
tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
tm.assert_index_equal(res, idx)
@pytest.mark.parametrize('arr, expected', [
(pd.DatetimeIndex(['2017', '2017']), pd.DatetimeIndex(['2017'])),
(pd.DatetimeIndex(['2017', '2017'], tz='US/Eastern'),
pd.DatetimeIndex(['2017'], tz='US/Eastern')),
])
def test_unique(self, arr, expected):
result = arr.unique()
tm.assert_index_equal(result, expected)
# GH 21737
# Ensure the underlying data is consistent
assert result[0] == expected[0]
def test_asarray_tz_naive(self):
# This shouldn't produce a warning.
idx = pd.date_range('2000', periods=2)
# M8[ns] by default
with tm.assert_produces_warning(None):
result = np.asarray(idx)
expected = np.array(['2000-01-01', '2000-01-02'], dtype='M8[ns]')
tm.assert_numpy_array_equal(result, expected)
# optionally, object
with tm.assert_produces_warning(None):
result = np.asarray(idx, dtype=object)
expected = np.array([pd.Timestamp('2000-01-01'),
pd.Timestamp('2000-01-02')])
tm.assert_numpy_array_equal(result, expected)
def test_asarray_tz_aware(self):
tz = 'US/Central'
idx = pd.date_range('2000', periods=2, tz=tz)
expected = np.array(['2000-01-01T06', '2000-01-02T06'], dtype='M8[ns]')
# We warn by default and return an ndarray[M8[ns]]
with tm.assert_produces_warning(FutureWarning):
result = np.asarray(idx)
tm.assert_numpy_array_equal(result, expected)
# Old behavior with no warning
with tm.assert_produces_warning(None):
result = np.asarray(idx, dtype="M8[ns]")
tm.assert_numpy_array_equal(result, expected)
# Future behavior with no warning
expected = np.array([pd.Timestamp("2000-01-01", tz=tz),
pd.Timestamp("2000-01-02", tz=tz)])
with tm.assert_produces_warning(None):
result = np.asarray(idx, dtype=object)
tm.assert_numpy_array_equal(result, expected)