1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
| import numpy as np
|
| from pandas import (
| DatetimeIndex,
| NaT,
| PeriodIndex,
| Series,
| TimedeltaIndex,
| date_range,
| period_range,
| timedelta_range,
| )
| import pandas._testing as tm
|
|
| class TestValueCounts:
| # GH#7735
|
| def test_value_counts_unique_datetimeindex(self, tz_naive_fixture):
| tz = tz_naive_fixture
| orig = date_range("2011-01-01 09:00", freq="H", periods=10, tz=tz)
| self._check_value_counts_with_repeats(orig)
|
| def test_value_counts_unique_timedeltaindex(self):
| orig = timedelta_range("1 days 09:00:00", freq="H", periods=10)
| self._check_value_counts_with_repeats(orig)
|
| def test_value_counts_unique_periodindex(self):
| orig = period_range("2011-01-01 09:00", freq="H", periods=10)
| self._check_value_counts_with_repeats(orig)
|
| def _check_value_counts_with_repeats(self, orig):
| # create repeated values, 'n'th element is repeated by n+1 times
| idx = type(orig)(
| np.repeat(orig._values, range(1, len(orig) + 1)), dtype=orig.dtype
| )
|
| exp_idx = orig[::-1]
| if not isinstance(exp_idx, PeriodIndex):
| exp_idx = exp_idx._with_freq(None)
| expected = Series(range(10, 0, -1), index=exp_idx, dtype="int64", name="count")
|
| for obj in [idx, Series(idx)]:
| tm.assert_series_equal(obj.value_counts(), expected)
|
| tm.assert_index_equal(idx.unique(), orig)
|
| def test_value_counts_unique_datetimeindex2(self, tz_naive_fixture):
| tz = tz_naive_fixture
| 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",
| NaT,
| ],
| tz=tz,
| )
| self._check_value_counts_dropna(idx)
|
| def test_value_counts_unique_timedeltaindex2(self):
| idx = TimedeltaIndex(
| [
| "1 days 09:00:00",
| "1 days 09:00:00",
| "1 days 09:00:00",
| "1 days 08:00:00",
| "1 days 08:00:00",
| NaT,
| ]
| )
| self._check_value_counts_dropna(idx)
|
| def test_value_counts_unique_periodindex2(self):
| idx = PeriodIndex(
| [
| "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",
| NaT,
| ],
| freq="H",
| )
| self._check_value_counts_dropna(idx)
|
| def _check_value_counts_dropna(self, idx):
| exp_idx = idx[[2, 3]]
| expected = Series([3, 2], index=exp_idx, name="count")
|
| for obj in [idx, Series(idx)]:
| tm.assert_series_equal(obj.value_counts(), expected)
|
| exp_idx = idx[[2, 3, -1]]
| expected = Series([3, 2, 1], index=exp_idx, name="count")
|
| for obj in [idx, Series(idx)]:
| tm.assert_series_equal(obj.value_counts(dropna=False), expected)
|
| tm.assert_index_equal(idx.unique(), exp_idx)
|
|
