Inzynierka_Gwiazdy/machine_learning/Lib/site-packages/pandas/tests/indexes/datetimelike.py
2023-09-20 19:46:58 +02:00

138 lines
4.2 KiB
Python

""" generic datetimelike tests """
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.tests.indexes.common import Base
class DatetimeLike(Base):
def test_isin(self, simple_index):
index = simple_index[:4]
result = index.isin(index)
assert result.all()
result = index.isin(list(index))
assert result.all()
result = index.isin([index[2], 5])
expected = np.array([False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
def test_argsort_matches_array(self, simple_index):
idx = simple_index
idx = idx.insert(1, pd.NaT)
result = idx.argsort()
expected = idx._data.argsort()
tm.assert_numpy_array_equal(result, expected)
def test_can_hold_identifiers(self, simple_index):
idx = simple_index
key = idx[0]
assert idx._can_hold_identifiers_and_holds_name(key) is False
def test_shift_identity(self, simple_index):
idx = simple_index
tm.assert_index_equal(idx, idx.shift(0))
def test_shift_empty(self, simple_index):
# GH#14811
idx = simple_index[:0]
tm.assert_index_equal(idx, idx.shift(1))
def test_str(self, simple_index):
# test the string repr
idx = simple_index
idx.name = "foo"
assert f"length={len(idx)}" not in str(idx)
assert "'foo'" in str(idx)
assert type(idx).__name__ in str(idx)
if hasattr(idx, "tz"):
if idx.tz is not None:
assert idx.tz in str(idx)
if isinstance(idx, pd.PeriodIndex):
assert f"dtype='period[{idx.freqstr}]'" in str(idx)
else:
assert f"freq='{idx.freqstr}'" in str(idx)
def test_view(self, simple_index):
idx = simple_index
idx_view = idx.view("i8")
result = self._index_cls(idx)
tm.assert_index_equal(result, idx)
idx_view = idx.view(self._index_cls)
result = self._index_cls(idx)
tm.assert_index_equal(result, idx_view)
def test_map_callable(self, simple_index):
index = simple_index
expected = index + index.freq
result = index.map(lambda x: x + x.freq)
tm.assert_index_equal(result, expected)
# map to NaT
result = index.map(lambda x: pd.NaT if x == index[0] else x)
expected = pd.Index([pd.NaT] + index[1:].tolist())
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"mapper",
[
lambda values, index: {i: e for e, i in zip(values, index)},
lambda values, index: pd.Series(values, index, dtype=object),
],
)
def test_map_dictlike(self, mapper, simple_index):
index = simple_index
expected = index + index.freq
# don't compare the freqs
if isinstance(expected, (pd.DatetimeIndex, pd.TimedeltaIndex)):
expected = expected._with_freq(None)
result = index.map(mapper(expected, index))
tm.assert_index_equal(result, expected)
expected = pd.Index([pd.NaT] + index[1:].tolist())
result = index.map(mapper(expected, index))
tm.assert_index_equal(result, expected)
# empty map; these map to np.nan because we cannot know
# to re-infer things
expected = pd.Index([np.nan] * len(index))
result = index.map(mapper([], []))
tm.assert_index_equal(result, expected)
def test_getitem_preserves_freq(self, simple_index):
index = simple_index
assert index.freq is not None
result = index[:]
assert result.freq == index.freq
def test_where_cast_str(self, simple_index):
index = simple_index
mask = np.ones(len(index), dtype=bool)
mask[-1] = False
result = index.where(mask, str(index[0]))
expected = index.where(mask, index[0])
tm.assert_index_equal(result, expected)
result = index.where(mask, [str(index[0])])
tm.assert_index_equal(result, expected)
expected = index.astype(object).where(mask, "foo")
result = index.where(mask, "foo")
tm.assert_index_equal(result, expected)
result = index.where(mask, ["foo"])
tm.assert_index_equal(result, expected)