def test_map_with_categorical_series(self):
# GH 12756
a = Index([1, 2, 3, 4])
b = Series(["even", "odd", "even", "odd"], dtype="category")
c = Series(["even", "odd", "even", "odd"])
exp = CategoricalIndex(["odd", "even", "odd", np.nan])
tm.assert_index_equal(a.map(b), exp)
exp = Index(["odd", "even", "odd", np.nan])
tm.assert_index_equal(a.map(c), exp)
def _get_skip_mask(cls, rows_index: pandas.Index, skiprows: Callable):
"""
Get mask of skipped by callable `skiprows` rows.
Parameters
----------
rows_index : pandas.Index
Rows index to get mask for.
skiprows : Callable
Callable to check whether row index should be skipped.
Returns
-------
pandas.Index
"""
try:
# direct `skiprows` call is more efficient than using of
# map method, but in some cases it can work incorrectly, e.g.
# when `skiprows` contains `in` operator
mask = skiprows(rows_index)
assert is_list_like(mask)
except (ValueError, TypeError, AssertionError):
# ValueError can be raised if `skiprows` callable contains membership operator
# TypeError is raised if `skiprows` callable contains bitwise operator
# AssertionError is raised if unexpected behavior was detected
mask = rows_index.map(skiprows)
return mask
def test_equals2(self):
# GH#13107
idx = TimedeltaIndex(["1 days", "2 days", "NaT"])
assert idx.equals(idx)
assert idx.equals(idx.copy())
assert idx.equals(idx.astype(object))
assert idx.astype(object).equals(idx)
assert idx.astype(object).equals(idx.astype(object))
assert not idx.equals(list(idx))
assert not idx.equals(pd.Series(idx))
idx2 = TimedeltaIndex(["2 days", "1 days", "NaT"])
assert not idx.equals(idx2)
assert not idx.equals(idx2.copy())
assert not idx.equals(idx2.astype(object))
assert not idx.astype(object).equals(idx2)
assert not idx.astype(object).equals(idx2.astype(object))
assert not idx.equals(list(idx2))
assert not idx.equals(pd.Series(idx2))
# Check that we dont raise OverflowError on comparisons outside the
# implementation range GH#28532
oob = Index([timedelta(days=10**6)] * 3, dtype=object)
assert not idx.equals(oob)
assert not idx2.equals(oob)
oob2 = Index([np.timedelta64(x) for x in oob], dtype=object)
assert (oob == oob2).all()
assert not idx.equals(oob2)
assert not idx2.equals(oob2)
oob3 = oob.map(np.timedelta64)
assert (oob3 == oob).all()
assert not idx.equals(oob3)
assert not idx2.equals(oob3)
def nanmap(columns: pd.Index, dict_map: dict) -> np.ndarray:
"""
columns.map(dict) では dict に定義されていない変数は nan になる.
定義されてないcolumnはそのまま残すようにする
"""
columns = columns.copy()
columnswk = columns.map(dict_map).fillna("__work").values
columns = columns.values
columns[(columnswk != "__work")] = columnswk[(columnswk != "__work")]
return columns
def test_equals(self):
# GH 13107
idx = DatetimeIndex(["2011-01-01", "2011-01-02", "NaT"])
assert idx.equals(idx)
assert idx.equals(idx.copy())
assert idx.equals(idx.astype(object))
assert idx.astype(object).equals(idx)
assert idx.astype(object).equals(idx.astype(object))
assert not idx.equals(list(idx))
assert not idx.equals(Series(idx))
idx2 = DatetimeIndex(["2011-01-01", "2011-01-02", "NaT"],
tz="US/Pacific")
assert not idx.equals(idx2)
assert not idx.equals(idx2.copy())
assert not idx.equals(idx2.astype(object))
assert not idx.astype(object).equals(idx2)
assert not idx.equals(list(idx2))
assert not idx.equals(Series(idx2))
# same internal, different tz
idx3 = DatetimeIndex(idx.asi8, tz="US/Pacific")
tm.assert_numpy_array_equal(idx.asi8, idx3.asi8)
assert not idx.equals(idx3)
assert not idx.equals(idx3.copy())
assert not idx.equals(idx3.astype(object))
assert not idx.astype(object).equals(idx3)
assert not idx.equals(list(idx3))
assert not idx.equals(Series(idx3))
# check that we do not raise when comparing with OutOfBounds objects
oob = Index([datetime(2500, 1, 1)] * 3, dtype=object)
assert not idx.equals(oob)
assert not idx2.equals(oob)
assert not idx3.equals(oob)
# check that we do not raise when comparing with OutOfBounds dt64
oob2 = oob.map(np.datetime64)
assert not idx.equals(oob2)
assert not idx2.equals(oob2)
assert not idx3.equals(oob2)
