419 lines
15 KiB
Python
419 lines
15 KiB
Python
from datetime import date, timedelta
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import dateutil
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import DataFrame, DatetimeIndex, Index, Timestamp, date_range, offsets
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import pandas._testing as tm
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randn = np.random.randn
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class TestDatetimeIndex:
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def test_reindex_preserves_tz_if_target_is_empty_list_or_array(self):
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# GH7774
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index = date_range("20130101", periods=3, tz="US/Eastern")
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assert str(index.reindex([])[0].tz) == "US/Eastern"
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assert str(index.reindex(np.array([]))[0].tz) == "US/Eastern"
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def test_reindex_with_same_tz(self):
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# GH 32740
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rng_a = date_range("2010-01-01", "2010-01-02", periods=24, tz="utc")
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rng_b = date_range("2010-01-01", "2010-01-02", periods=23, tz="utc")
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result1, result2 = rng_a.reindex(
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rng_b, method="nearest", tolerance=timedelta(seconds=20)
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)
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expected_list1 = [
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"2010-01-01 00:00:00",
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"2010-01-01 01:05:27.272727272",
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"2010-01-01 02:10:54.545454545",
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"2010-01-01 03:16:21.818181818",
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"2010-01-01 04:21:49.090909090",
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"2010-01-01 05:27:16.363636363",
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"2010-01-01 06:32:43.636363636",
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"2010-01-01 07:38:10.909090909",
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"2010-01-01 08:43:38.181818181",
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"2010-01-01 09:49:05.454545454",
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"2010-01-01 10:54:32.727272727",
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"2010-01-01 12:00:00",
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"2010-01-01 13:05:27.272727272",
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"2010-01-01 14:10:54.545454545",
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"2010-01-01 15:16:21.818181818",
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"2010-01-01 16:21:49.090909090",
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"2010-01-01 17:27:16.363636363",
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"2010-01-01 18:32:43.636363636",
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"2010-01-01 19:38:10.909090909",
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"2010-01-01 20:43:38.181818181",
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"2010-01-01 21:49:05.454545454",
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"2010-01-01 22:54:32.727272727",
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"2010-01-02 00:00:00",
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]
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expected1 = DatetimeIndex(
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expected_list1, dtype="datetime64[ns, UTC]", freq=None
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)
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expected2 = np.array([0] + [-1] * 21 + [23], dtype=np.dtype("intp"))
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tm.assert_index_equal(result1, expected1)
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tm.assert_numpy_array_equal(result2, expected2)
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def test_time_loc(self): # GH8667
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from datetime import time
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from pandas._libs.index import _SIZE_CUTOFF
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ns = _SIZE_CUTOFF + np.array([-100, 100], dtype=np.int64)
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key = time(15, 11, 30)
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start = key.hour * 3600 + key.minute * 60 + key.second
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step = 24 * 3600
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for n in ns:
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idx = date_range("2014-11-26", periods=n, freq="S")
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ts = pd.Series(np.random.randn(n), index=idx)
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i = np.arange(start, n, step)
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tm.assert_numpy_array_equal(ts.index.get_loc(key), i, check_dtype=False)
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tm.assert_series_equal(ts[key], ts.iloc[i])
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left, right = ts.copy(), ts.copy()
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left[key] *= -10
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right.iloc[i] *= -10
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tm.assert_series_equal(left, right)
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def test_time_overflow_for_32bit_machines(self):
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# GH8943. On some machines NumPy defaults to np.int32 (for example,
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# 32-bit Linux machines). In the function _generate_regular_range
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# found in tseries/index.py, `periods` gets multiplied by `strides`
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# (which has value 1e9) and since the max value for np.int32 is ~2e9,
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# and since those machines won't promote np.int32 to np.int64, we get
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# overflow.
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periods = np.int_(1000)
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idx1 = date_range(start="2000", periods=periods, freq="S")
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assert len(idx1) == periods
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idx2 = date_range(end="2000", periods=periods, freq="S")
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assert len(idx2) == periods
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def test_nat(self):
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assert DatetimeIndex([np.nan])[0] is pd.NaT
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def test_week_of_month_frequency(self):
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# GH 5348: "ValueError: Could not evaluate WOM-1SUN" shouldn't raise
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d1 = date(2002, 9, 1)
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d2 = date(2013, 10, 27)
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d3 = date(2012, 9, 30)
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idx1 = DatetimeIndex([d1, d2])
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idx2 = DatetimeIndex([d3])
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result_append = idx1.append(idx2)
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expected = DatetimeIndex([d1, d2, d3])
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tm.assert_index_equal(result_append, expected)
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result_union = idx1.union(idx2)
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expected = DatetimeIndex([d1, d3, d2])
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tm.assert_index_equal(result_union, expected)
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# GH 5115
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result = date_range("2013-1-1", periods=4, freq="WOM-1SAT")
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dates = ["2013-01-05", "2013-02-02", "2013-03-02", "2013-04-06"]
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expected = DatetimeIndex(dates, freq="WOM-1SAT")
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tm.assert_index_equal(result, expected)
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def test_stringified_slice_with_tz(self):
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# GH#2658
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start = "2013-01-07"
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idx = date_range(start=start, freq="1d", periods=10, tz="US/Eastern")
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df = DataFrame(np.arange(10), index=idx)
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df["2013-01-14 23:44:34.437768-05:00":] # no exception here
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def test_append_nondatetimeindex(self):
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rng = date_range("1/1/2000", periods=10)
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idx = Index(["a", "b", "c", "d"])
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result = rng.append(idx)
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assert isinstance(result[0], Timestamp)
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def test_iteration_preserves_tz(self):
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# see gh-8890
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index = date_range("2012-01-01", periods=3, freq="H", tz="US/Eastern")
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for i, ts in enumerate(index):
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result = ts
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expected = index[i]
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assert result == expected
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index = date_range(
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"2012-01-01", periods=3, freq="H", tz=dateutil.tz.tzoffset(None, -28800)
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)
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for i, ts in enumerate(index):
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result = ts
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expected = index[i]
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assert result._repr_base == expected._repr_base
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assert result == expected
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# 9100
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index = DatetimeIndex(
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["2014-12-01 03:32:39.987000-08:00", "2014-12-01 04:12:34.987000-08:00"]
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)
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for i, ts in enumerate(index):
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result = ts
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expected = index[i]
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assert result._repr_base == expected._repr_base
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assert result == expected
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@pytest.mark.parametrize("periods", [0, 9999, 10000, 10001])
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def test_iteration_over_chunksize(self, periods):
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# GH21012
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index = date_range("2000-01-01 00:00:00", periods=periods, freq="min")
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num = 0
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for stamp in index:
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assert index[num] == stamp
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num += 1
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assert num == len(index)
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def test_misc_coverage(self):
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rng = date_range("1/1/2000", periods=5)
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result = rng.groupby(rng.day)
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assert isinstance(list(result.values())[0][0], Timestamp)
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def test_string_index_series_name_converted(self):
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# #1644
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df = DataFrame(np.random.randn(10, 4), index=date_range("1/1/2000", periods=10))
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result = df.loc["1/3/2000"]
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assert result.name == df.index[2]
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result = df.T["1/3/2000"]
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assert result.name == df.index[2]
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def test_argmin_argmax(self):
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idx = DatetimeIndex(["2000-01-04", "2000-01-01", "2000-01-02"])
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assert idx.argmin() == 1
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assert idx.argmax() == 0
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def test_sort_values(self):
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idx = DatetimeIndex(["2000-01-04", "2000-01-01", "2000-01-02"])
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ordered = idx.sort_values()
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assert ordered.is_monotonic
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ordered = idx.sort_values(ascending=False)
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assert ordered[::-1].is_monotonic
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ordered, dexer = idx.sort_values(return_indexer=True)
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assert ordered.is_monotonic
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tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0], dtype=np.intp))
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ordered, dexer = idx.sort_values(return_indexer=True, ascending=False)
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assert ordered[::-1].is_monotonic
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tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1], dtype=np.intp))
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def test_groupby_function_tuple_1677(self):
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df = DataFrame(np.random.rand(100), index=date_range("1/1/2000", periods=100))
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monthly_group = df.groupby(lambda x: (x.year, x.month))
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result = monthly_group.mean()
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assert isinstance(result.index[0], tuple)
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def test_append_numpy_bug_1681(self):
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# another datetime64 bug
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dr = date_range("2011/1/1", "2012/1/1", freq="W-FRI")
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a = DataFrame()
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c = DataFrame({"A": "foo", "B": dr}, index=dr)
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result = a.append(c)
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assert (result["B"] == dr).all()
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def test_isin(self):
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index = tm.makeDateIndex(4)
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result = index.isin(index)
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assert result.all()
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result = index.isin(list(index))
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assert result.all()
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tm.assert_almost_equal(
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index.isin([index[2], 5]), np.array([False, False, True, False])
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)
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def assert_index_parameters(self, index):
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assert index.freq == "40960N"
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assert index.inferred_freq == "40960N"
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def test_ns_index(self):
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nsamples = 400
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ns = int(1e9 / 24414)
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dtstart = np.datetime64("2012-09-20T00:00:00")
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dt = dtstart + np.arange(nsamples) * np.timedelta64(ns, "ns")
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freq = ns * offsets.Nano()
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index = DatetimeIndex(dt, freq=freq, name="time")
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self.assert_index_parameters(index)
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new_index = date_range(start=index[0], end=index[-1], freq=index.freq)
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self.assert_index_parameters(new_index)
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def test_factorize(self):
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idx1 = DatetimeIndex(
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["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"]
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)
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exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
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exp_idx = DatetimeIndex(["2014-01", "2014-02", "2014-03"])
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arr, idx = idx1.factorize()
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, exp_idx)
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assert idx.freq == exp_idx.freq
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arr, idx = idx1.factorize(sort=True)
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, exp_idx)
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assert idx.freq == exp_idx.freq
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# tz must be preserved
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idx1 = idx1.tz_localize("Asia/Tokyo")
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exp_idx = exp_idx.tz_localize("Asia/Tokyo")
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arr, idx = idx1.factorize()
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, exp_idx)
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assert idx.freq == exp_idx.freq
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idx2 = DatetimeIndex(
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["2014-03", "2014-03", "2014-02", "2014-01", "2014-03", "2014-01"]
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)
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exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.intp)
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exp_idx = DatetimeIndex(["2014-01", "2014-02", "2014-03"])
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arr, idx = idx2.factorize(sort=True)
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, exp_idx)
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assert idx.freq == exp_idx.freq
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exp_arr = np.array([0, 0, 1, 2, 0, 2], dtype=np.intp)
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exp_idx = DatetimeIndex(["2014-03", "2014-02", "2014-01"])
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arr, idx = idx2.factorize()
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, exp_idx)
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assert idx.freq == exp_idx.freq
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def test_factorize_preserves_freq(self):
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# GH#38120 freq should be preserved
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idx3 = date_range("2000-01", periods=4, freq="M", tz="Asia/Tokyo")
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exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
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arr, idx = idx3.factorize()
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, idx3)
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assert idx.freq == idx3.freq
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arr, idx = pd.factorize(idx3)
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, idx3)
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assert idx.freq == idx3.freq
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def test_factorize_tz(self, tz_naive_fixture, index_or_series):
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tz = tz_naive_fixture
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# GH#13750
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base = date_range("2016-11-05", freq="H", periods=100, tz=tz)
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idx = base.repeat(5)
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exp_arr = np.arange(100, dtype=np.intp).repeat(5)
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obj = index_or_series(idx)
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arr, res = obj.factorize()
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tm.assert_numpy_array_equal(arr, exp_arr)
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expected = base._with_freq(None)
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tm.assert_index_equal(res, expected)
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assert res.freq == expected.freq
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def test_factorize_dst(self, index_or_series):
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# GH 13750
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idx = date_range("2016-11-06", freq="H", periods=12, tz="US/Eastern")
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obj = index_or_series(idx)
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arr, res = obj.factorize()
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tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
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tm.assert_index_equal(res, idx)
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if index_or_series is Index:
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assert res.freq == idx.freq
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idx = date_range("2016-06-13", freq="H", periods=12, tz="US/Eastern")
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obj = index_or_series(idx)
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arr, res = obj.factorize()
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tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
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tm.assert_index_equal(res, idx)
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if index_or_series is Index:
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assert res.freq == idx.freq
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@pytest.mark.parametrize(
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"arr, expected",
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[
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(DatetimeIndex(["2017", "2017"]), DatetimeIndex(["2017"])),
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(
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DatetimeIndex(["2017", "2017"], tz="US/Eastern"),
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DatetimeIndex(["2017"], tz="US/Eastern"),
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),
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],
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)
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def test_unique(self, arr, expected):
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result = arr.unique()
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tm.assert_index_equal(result, expected)
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# GH 21737
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# Ensure the underlying data is consistent
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assert result[0] == expected[0]
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def test_asarray_tz_naive(self):
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# This shouldn't produce a warning.
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idx = date_range("2000", periods=2)
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# M8[ns] by default
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result = np.asarray(idx)
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expected = np.array(["2000-01-01", "2000-01-02"], dtype="M8[ns]")
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tm.assert_numpy_array_equal(result, expected)
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# optionally, object
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result = np.asarray(idx, dtype=object)
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expected = np.array([pd.Timestamp("2000-01-01"), pd.Timestamp("2000-01-02")])
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tm.assert_numpy_array_equal(result, expected)
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def test_asarray_tz_aware(self):
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tz = "US/Central"
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idx = date_range("2000", periods=2, tz=tz)
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expected = np.array(["2000-01-01T06", "2000-01-02T06"], dtype="M8[ns]")
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result = np.asarray(idx, dtype="datetime64[ns]")
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tm.assert_numpy_array_equal(result, expected)
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# Old behavior with no warning
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result = np.asarray(idx, dtype="M8[ns]")
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tm.assert_numpy_array_equal(result, expected)
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# Future behavior with no warning
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expected = np.array(
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[pd.Timestamp("2000-01-01", tz=tz), pd.Timestamp("2000-01-02", tz=tz)]
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)
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result = np.asarray(idx, dtype=object)
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tm.assert_numpy_array_equal(result, expected)
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def test_to_frame_datetime_tz(self):
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# GH 25809
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idx = date_range(start="2019-01-01", end="2019-01-30", freq="D", tz="UTC")
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result = idx.to_frame()
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expected = DataFrame(idx, index=idx)
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tm.assert_frame_equal(result, expected)
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def test_split_non_utc(self):
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# GH 14042
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indices = date_range("2016-01-01 00:00:00+0200", freq="S", periods=10)
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result = np.split(indices, indices_or_sections=[])[0]
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expected = indices._with_freq(None)
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tm.assert_index_equal(result, expected)
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