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test_datetime.py
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from datetime import date, timedelta
import dateutil
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, DatetimeIndex, Index, NaT, Timestamp, date_range, offsets
import pandas._testing as tm
randn = np.random.randn
class TestDatetimeIndex:
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_pickle(self):
# GH#4606
p = tm.round_trip_pickle(NaT)
assert p is NaT
idx = pd.to_datetime(["2013-01-01", NaT, "2014-01-06"])
idx_p = tm.round_trip_pickle(idx)
assert idx_p[0] == idx[0]
assert idx_p[1] is NaT
assert idx_p[2] == idx[2]
# GH#11002
# don't infer freq
idx = date_range("1750-1-1", "2050-1-1", freq="7D")
idx_p = tm.round_trip_pickle(idx)
tm.assert_index_equal(idx, idx_p)
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_reindex_with_same_tz(self):
# GH 32740
rng_a = date_range("2010-01-01", "2010-01-02", periods=24, tz="utc")
rng_b = date_range("2010-01-01", "2010-01-02", periods=23, tz="utc")
result1, result2 = rng_a.reindex(
rng_b, method="nearest", tolerance=timedelta(seconds=20)
)
expected_list1 = [
"2010-01-01 00:00:00",
"2010-01-01 01:05:27.272727272",
"2010-01-01 02:10:54.545454545",
"2010-01-01 03:16:21.818181818",
"2010-01-01 04:21:49.090909090",
"2010-01-01 05:27:16.363636363",
"2010-01-01 06:32:43.636363636",
"2010-01-01 07:38:10.909090909",
"2010-01-01 08:43:38.181818181",
"2010-01-01 09:49:05.454545454",
"2010-01-01 10:54:32.727272727",
"2010-01-01 12:00:00",
"2010-01-01 13:05:27.272727272",
"2010-01-01 14:10:54.545454545",
"2010-01-01 15:16:21.818181818",
"2010-01-01 16:21:49.090909090",
"2010-01-01 17:27:16.363636363",
"2010-01-01 18:32:43.636363636",
"2010-01-01 19:38:10.909090909",
"2010-01-01 20:43:38.181818181",
"2010-01-01 21:49:05.454545454",
"2010-01-01 22:54:32.727272727",
"2010-01-02 00:00:00",
]
expected1 = DatetimeIndex(
expected_list1, dtype="datetime64[ns, UTC]", freq=None,
)
expected2 = np.array([0] + [-1] * 21 + [23], dtype=np.int64,)
tm.assert_index_equal(result1, expected1)
tm.assert_numpy_array_equal(result2, expected2)
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_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(np.arange(10), index=idx)
df["2013-01-14 23:44:34.437768-05:00":] # no exception here
def test_append_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)
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_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()
tm.assert_almost_equal(
index.isin([index[2], 5]), np.array([False, False, True, False])
)
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_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)
expected = base._with_freq(None)
tm.assert_index_equal(res, expected)
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
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
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]")
result = np.asarray(idx, dtype="datetime64[ns]")
tm.assert_numpy_array_equal(result, expected)
# Old behavior with no warning
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)]
)
result = np.asarray(idx, dtype=object)
tm.assert_numpy_array_equal(result, expected)
def test_to_frame_datetime_tz(self):
# GH 25809
idx = date_range(start="2019-01-01", end="2019-01-30", freq="D", tz="UTC")
result = idx.to_frame()
expected = DataFrame(idx, index=idx)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("name", [None, "name"])
def test_index_map(self, name):
# see GH20990
count = 6
index = pd.date_range("2018-01-01", periods=count, freq="M", name=name).map(
lambda x: (x.year, x.month)
)
exp_index = pd.MultiIndex.from_product(
((2018,), range(1, 7)), names=[name, name]
)
tm.assert_index_equal(index, exp_index)
def test_split_non_utc(self):
# GH 14042
indices = pd.date_range("2016-01-01 00:00:00+0200", freq="S", periods=10)
result = np.split(indices, indices_or_sections=[])[0]
expected = indices.copy()
expected._set_freq(None)
tm.assert_index_equal(result, expected)