def test_to_timestamp_to_period_astype(self):
idx = DatetimeIndex([pd.NaT, '2011-01-01', '2011-02-01'], name='idx')
res = idx.astype('period[M]')
exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='M', name='idx')
tm.assert_index_equal(res, exp)
res = idx.astype('period[3M]')
exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='3M', name='idx')
tm.assert_index_equal(res, exp)
def test_dti_astype_period(self):
idx = DatetimeIndex([NaT, "2011-01-01", "2011-02-01"], name="idx")
res = idx.astype("period[M]")
exp = PeriodIndex(["NaT", "2011-01", "2011-02"], freq="M", name="idx")
tm.assert_index_equal(res, exp)
res = idx.astype("period[3M]")
exp = PeriodIndex(["NaT", "2011-01", "2011-02"], freq="3M", name="idx")
tm.assert_index_equal(res, exp)
def test_to_timestamp_to_period_astype(self):
idx = DatetimeIndex([pd.NaT, '2011-01-01', '2011-02-01'], name='idx')
res = idx.astype('period[M]')
exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='M', name='idx')
tm.assert_index_equal(res, exp)
res = idx.astype('period[3M]')
exp = PeriodIndex(['NaT', '2011-01', '2011-02'], freq='3M', name='idx')
tm.assert_index_equal(res, exp)
def test_astype(self):
# GH 13149, GH 13209
idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN])
result = idx.astype(object)
expected = Index([Timestamp('2016-05-16')] + [NaT] * 3, dtype=object)
tm.assert_index_equal(result, expected)
result = idx.astype(int)
expected = Int64Index([1463356800000000000] +
[-9223372036854775808] * 3, dtype=np.int64)
tm.assert_index_equal(result, expected)
rng = date_range('1/1/2000', periods=10)
result = rng.astype('i8')
self.assert_numpy_array_equal(result, rng.asi8)
def date_to_timestamp(date: pd.DatetimeIndex):
"""Convert a pandas datetime to a javascript timestamp.
This aims to match the timezone handling semantics
used in Vega and Vega-Lite.
Parameters
----------
timestamp : float
The unix epoch timestamp.
Returns
-------
date : pd.DatetimeIndex
The timestamps to be converted
See Also
--------
date_to_timestamp : opposite of this function
"""
if date.tzinfo is None:
date = date.tz_localize(tzlocal())
try:
# Works for pd.Timestamp
return date.timestamp() * 1000
except AttributeError:
# Works for pd.DatetimeIndex
return date.astype('int64') * 1E-6
def get_fwd_rates(d1, d2, fwd_curve, cal, roll_conv):
# Get historical fixings
historical_dates = d1[d1 <= fwd_curve.curve_date]
historical_fixings = []
historical_fixing_dates = []
for i,d in enumerate(historical_dates):
if d in fwd_curve.historical_fixings.keys():
historical_fixings.append(fwd_curve.historical_fixings[d]/100.0)
historical_fixing_dates.append(d)
else:
j = 1
while j < 10:
d = DatetimeIndex([d])
d = pd.Timestamp(np.busday_offset(dates=d.astype(str), offsets=j, roll=roll_conv, busdaycal=cal)[0])
if d in fwd_curve.historical_fixings.keys():
historical_fixings.append(fwd_curve.historical_fixings[d]/100.0)
historical_fixing_dates.append(d)
break
j += 1
# Calculate future fixings
d2 = d2[d1 > fwd_curve.curve_date]
d1 = d1[d1 > fwd_curve.curve_date]
t = pd.Series(sch.year_fraction(d1,d2,fwd_curve.day_count_basis))
future_fixings = list((1 / t) * (fwd_curve.get_dcf(d1) / fwd_curve.get_dcf(d2) - 1))
return pd.Series(historical_fixing_dates + d1.to_list()), pd.Series(historical_fixings + future_fixings)
def test_astype_datetime64(self):
# GH 13149, GH 13209
idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN])
result = idx.astype("datetime64[ns]")
tm.assert_index_equal(result, idx)
assert result is not idx
result = idx.astype("datetime64[ns]", copy=False)
tm.assert_index_equal(result, idx)
assert result is idx
idx_tz = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], tz="EST")
result = idx_tz.astype("datetime64[ns]")
expected = DatetimeIndex(["2016-05-16 05:00:00", "NaT", "NaT", "NaT"],
dtype="datetime64[ns]")
tm.assert_index_equal(result, expected)
def test_astype_datetime64(self):
# GH 13149, GH 13209
idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN])
result = idx.astype('datetime64[ns]')
tm.assert_index_equal(result, idx)
assert result is not idx
result = idx.astype('datetime64[ns]', copy=False)
tm.assert_index_equal(result, idx)
assert result is idx
idx_tz = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN], tz='EST')
result = idx_tz.astype('datetime64[ns]')
expected = DatetimeIndex(['2016-05-16 05:00:00', 'NaT', 'NaT', 'NaT'],
dtype='datetime64[ns]')
tm.assert_index_equal(result, expected)
def test_astype(self):
# GH 13149, GH 13209
idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN])
result = idx.astype(object)
expected = Index([Timestamp('2016-05-16')] + [NaT] * 3, dtype=object)
tm.assert_index_equal(result, expected)
result = idx.astype(int)
expected = Int64Index([1463356800000000000] +
[-9223372036854775808] * 3, dtype=np.int64)
tm.assert_index_equal(result, expected)
rng = date_range('1/1/2000', periods=10)
result = rng.astype('i8')
tm.assert_index_equal(result, Index(rng.asi8))
tm.assert_numpy_array_equal(result.values, rng.asi8)
def test_astype_str_compat(self):
# GH 13149, GH 13209
# verify that we are returning NaT as a string (and not unicode)
idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN])
result = idx.astype(str)
expected = Index(['2016-05-16', 'NaT', 'NaT', 'NaT'], dtype=object)
tm.assert_index_equal(result, expected)
def test_astype_object(self):
idx = pd.date_range(start='2013-01-01',
periods=4,
freq='M',
name='idx')
expected_list = [
Timestamp('2013-01-31'),
Timestamp('2013-02-28'),
Timestamp('2013-03-31'),
Timestamp('2013-04-30')
]
expected = pd.Index(expected_list, dtype=object, name='idx')
result = idx.astype(object)
assert isinstance(result, Index)
assert result.dtype == object
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert idx.tolist() == expected_list
idx = pd.date_range(start='2013-01-01',
periods=4,
freq='M',
name='idx',
tz='Asia/Tokyo')
expected_list = [
Timestamp('2013-01-31', tz='Asia/Tokyo'),
Timestamp('2013-02-28', tz='Asia/Tokyo'),
Timestamp('2013-03-31', tz='Asia/Tokyo'),
Timestamp('2013-04-30', tz='Asia/Tokyo')
]
expected = pd.Index(expected_list, dtype=object, name='idx')
result = idx.astype(object)
assert isinstance(result, Index)
assert result.dtype == object
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert idx.tolist() == expected_list
idx = DatetimeIndex([
datetime(2013, 1, 1),
datetime(2013, 1, 2), pd.NaT,
datetime(2013, 1, 4)
],
name='idx')
expected_list = [
Timestamp('2013-01-01'),
Timestamp('2013-01-02'), pd.NaT,
Timestamp('2013-01-04')
]
expected = pd.Index(expected_list, dtype=object, name='idx')
result = idx.astype(object)
assert isinstance(result, Index)
assert result.dtype == object
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert idx.tolist() == expected_list
def test_astype_object_with_nat(self):
idx = DatetimeIndex([datetime(2013, 1, 1), datetime(2013, 1, 2),
pd.NaT, datetime(2013, 1, 4)], name='idx')
expected_list = [Timestamp('2013-01-01'),
Timestamp('2013-01-02'), pd.NaT,
Timestamp('2013-01-04')]
expected = pd.Index(expected_list, dtype=object, name='idx')
result = idx.astype(object)
tm.assert_index_equal(result, expected)
assert idx.tolist() == expected_list
def test_astype_object_with_nat(self):
idx = DatetimeIndex([datetime(2013, 1, 1), datetime(2013, 1, 2),
pd.NaT, datetime(2013, 1, 4)], name='idx')
expected_list = [Timestamp('2013-01-01'),
Timestamp('2013-01-02'), pd.NaT,
Timestamp('2013-01-04')]
expected = pd.Index(expected_list, dtype=object, name='idx')
result = idx.astype(object)
tm.assert_index_equal(result, expected)
assert idx.tolist() == expected_list
def test_astype_datetime64(self):
# GH 13149, GH 13209
idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], name="idx")
result = idx.astype("datetime64[ns]")
tm.assert_index_equal(result, idx)
assert result is not idx
result = idx.astype("datetime64[ns]", copy=False)
tm.assert_index_equal(result, idx)
assert result is idx
idx_tz = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], tz="EST", name="idx")
with tm.assert_produces_warning(FutureWarning):
# dt64tz->dt64 deprecated
result = idx_tz.astype("datetime64[ns]")
expected = DatetimeIndex(
["2016-05-16 05:00:00", "NaT", "NaT", "NaT"],
dtype="datetime64[ns]",
name="idx",
)
tm.assert_index_equal(result, expected)
def test_astype(self):
# GH 13149, GH 13209
idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], name="idx")
result = idx.astype(object)
expected = Index(
[Timestamp("2016-05-16")] + [NaT] * 3, dtype=object, name="idx"
)
tm.assert_index_equal(result, expected)
result = idx.astype(int)
expected = Int64Index(
[1463356800000000000] + [-9223372036854775808] * 3,
dtype=np.int64,
name="idx",
)
tm.assert_index_equal(result, expected)
rng = date_range("1/1/2000", periods=10, name="idx")
result = rng.astype("i8")
tm.assert_index_equal(result, Index(rng.asi8, name="idx"))
tm.assert_numpy_array_equal(result.values, rng.asi8)
def test_astype_object_with_nat(self):
idx = DatetimeIndex(
[datetime(2013, 1, 1), datetime(2013, 1, 2), NaT, datetime(2013, 1, 4)],
name="idx",
)
expected_list = [
Timestamp("2013-01-01"),
Timestamp("2013-01-02"),
NaT,
Timestamp("2013-01-04"),
]
expected = Index(expected_list, dtype=object, name="idx")
result = idx.astype(object)
tm.assert_index_equal(result, expected)
assert idx.tolist() == expected_list
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)
def test_astype_object(self):
idx = pd.date_range(start='2013-01-01', periods=4, freq='M',
name='idx')
expected_list = [Timestamp('2013-01-31'),
Timestamp('2013-02-28'),
Timestamp('2013-03-31'),
Timestamp('2013-04-30')]
expected = pd.Index(expected_list, dtype=object, name='idx')
result = idx.astype(object)
assert isinstance(result, Index)
assert result.dtype == object
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert idx.tolist() == expected_list
idx = pd.date_range(start='2013-01-01', periods=4, freq='M',
name='idx', tz='Asia/Tokyo')
expected_list = [Timestamp('2013-01-31', tz='Asia/Tokyo'),
Timestamp('2013-02-28', tz='Asia/Tokyo'),
Timestamp('2013-03-31', tz='Asia/Tokyo'),
Timestamp('2013-04-30', tz='Asia/Tokyo')]
expected = pd.Index(expected_list, dtype=object, name='idx')
result = idx.astype(object)
assert isinstance(result, Index)
assert result.dtype == object
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert idx.tolist() == expected_list
idx = DatetimeIndex([datetime(2013, 1, 1), datetime(2013, 1, 2),
pd.NaT, datetime(2013, 1, 4)], name='idx')
expected_list = [Timestamp('2013-01-01'),
Timestamp('2013-01-02'), pd.NaT,
Timestamp('2013-01-04')]
expected = pd.Index(expected_list, dtype=object, name='idx')
result = idx.astype(object)
assert isinstance(result, Index)
assert result.dtype == object
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert idx.tolist() == expected_list
def test_astype_raises(self, dtype):
# GH 13149, GH 13209
idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN])
msg = 'Cannot cast DatetimeArrayMixin to dtype'
with pytest.raises(TypeError, match=msg):
idx.astype(dtype)
def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
"""
Encode input values as an enumerated type or categorical variable
Parameters
----------
values : ndarray (1-d)
Sequence
sort : boolean, default False
Sort by values
na_sentinel : int, default -1
Value to mark "not found"
size_hint : hint to the hashtable sizer
Returns
-------
labels : the indexer to the original array
uniques : ndarray (1-d) or Index
the unique values. Index is returned when passed values is Index or
Series
note: an array of Periods will ignore sort as it returns an always sorted
PeriodIndex
"""
from pandas import Index, Series, DatetimeIndex
vals = np.asarray(values)
# localize to UTC
is_datetimetz_type = is_datetimetz(values)
if is_datetimetz_type:
values = DatetimeIndex(values)
vals = values.tz_localize(None)
is_datetime = is_datetime64_dtype(vals)
is_timedelta = is_timedelta64_dtype(vals)
(hash_klass, vec_klass), vals = _get_data_algo(vals, _hashtables)
table = hash_klass(size_hint or len(vals))
uniques = vec_klass()
labels = table.get_labels(vals, uniques, 0, na_sentinel, True)
labels = _ensure_platform_int(labels)
uniques = uniques.to_array()
if sort and len(uniques) > 0:
uniques, labels = safe_sort(uniques, labels, na_sentinel=na_sentinel,
assume_unique=True)
if is_datetimetz_type:
# reset tz
uniques = DatetimeIndex(uniques.astype('M8[ns]')).tz_localize(
values.tz)
elif is_datetime:
uniques = uniques.astype('M8[ns]')
elif is_timedelta:
uniques = uniques.astype('m8[ns]')
if isinstance(values, Index):
uniques = values._shallow_copy(uniques, name=None)
elif isinstance(values, Series):
uniques = Index(uniques)
return labels, uniques
def test_astype_raises(self, dtype):
# GH 13149, GH 13209
idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN])
msg = 'Cannot cast DatetimeIndex to dtype'
with tm.assert_raises_regex(TypeError, msg):
idx.astype(dtype)
def value_counts(values,
sort=True,
ascending=False,
normalize=False,
bins=None,
dropna=True):
"""
Compute a histogram of the counts of non-null values.
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
normalize: boolean, default False
If True then compute a relative histogram
bins : integer, optional
Rather than count values, group them into half-open bins,
convenience for pd.cut, only works with numeric data
dropna : boolean, default True
Don't include counts of NaN
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
from pandas.tools.tile import cut
from pandas import Index, PeriodIndex, DatetimeIndex
name = getattr(values, 'name', None)
values = Series(values).values
if bins is not None:
try:
cat, bins = cut(values, bins, retbins=True)
except TypeError:
raise TypeError("bins argument only works with numeric data.")
values = cat.codes
if com.is_categorical_dtype(values.dtype):
result = values.value_counts(dropna)
else:
dtype = values.dtype
is_period = com.is_period_arraylike(values)
is_datetimetz = com.is_datetimetz(values)
if com.is_datetime_or_timedelta_dtype(
dtype) or is_period or is_datetimetz:
if is_period:
values = PeriodIndex(values)
elif is_datetimetz:
tz = getattr(values, 'tz', None)
values = DatetimeIndex(values).tz_localize(None)
values = values.view(np.int64)
keys, counts = htable.value_count_scalar64(values, dropna)
if dropna:
from pandas.tslib import iNaT
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
# localize to the original tz if necessary
if is_datetimetz:
keys = DatetimeIndex(keys).tz_localize(tz)
# convert the keys back to the dtype we came in
else:
keys = keys.astype(dtype)
elif com.is_integer_dtype(dtype):
values = com._ensure_int64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
elif com.is_float_dtype(dtype):
values = com._ensure_float64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
else:
values = com._ensure_object(values)
mask = com.isnull(values)
keys, counts = htable.value_count_object(values, mask)
if not dropna and mask.any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
if not isinstance(keys, Index):
keys = Index(keys)
result = Series(counts, index=keys, name=name)
if bins is not None:
# TODO: This next line should be more efficient
result = result.reindex(np.arange(len(cat.categories)),
fill_value=0)
result.index = bins[:-1]
if sort:
result = result.sort_values(ascending=ascending)
if normalize:
result = result / float(values.size)
return result
def test_astype_raises(self, dtype):
# GH 13149, GH 13209
idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN])
msg = 'Cannot cast DatetimeArray to dtype'
with pytest.raises(TypeError, match=msg):
idx.astype(dtype)
def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
"""
Encode input values as an enumerated type or categorical variable
Parameters
----------
values : ndarray (1-d)
Sequence
sort : boolean, default False
Sort by values
na_sentinel : int, default -1
Value to mark "not found"
size_hint : hint to the hashtable sizer
Returns
-------
labels : the indexer to the original array
uniques : ndarray (1-d) or Index
the unique values. Index is returned when passed values is Index or
Series
note: an array of Periods will ignore sort as it returns an always sorted
PeriodIndex
"""
from pandas import Index, Series, DatetimeIndex
vals = np.asarray(values)
# localize to UTC
is_datetimetz = com.is_datetimetz(values)
if is_datetimetz:
values = DatetimeIndex(values)
vals = values.tz_localize(None)
is_datetime = com.is_datetime64_dtype(vals)
is_timedelta = com.is_timedelta64_dtype(vals)
(hash_klass, vec_klass), vals = _get_data_algo(vals, _hashtables)
table = hash_klass(size_hint or len(vals))
uniques = vec_klass()
labels = table.get_labels(vals, uniques, 0, na_sentinel, True)
labels = com._ensure_platform_int(labels)
uniques = uniques.to_array()
if sort and len(uniques) > 0:
try:
sorter = uniques.argsort()
except:
# unorderable in py3 if mixed str/int
t = hash_klass(len(uniques))
t.map_locations(com._ensure_object(uniques))
# order ints before strings
ordered = np.concatenate([
np.sort(np.array([e for i, e in enumerate(uniques) if f(e)],
dtype=object)) for f in
[lambda x: not isinstance(x, string_types),
lambda x: isinstance(x, string_types)]])
sorter = com._ensure_platform_int(t.lookup(
com._ensure_object(ordered)))
reverse_indexer = np.empty(len(sorter), dtype=np.int_)
reverse_indexer.put(sorter, np.arange(len(sorter)))
mask = labels < 0
labels = reverse_indexer.take(labels)
np.putmask(labels, mask, -1)
uniques = uniques.take(sorter)
if is_datetimetz:
# reset tz
uniques = DatetimeIndex(uniques.astype('M8[ns]')).tz_localize(
values.tz)
elif is_datetime:
uniques = uniques.astype('M8[ns]')
elif is_timedelta:
uniques = uniques.astype('m8[ns]')
if isinstance(values, Index):
uniques = values._shallow_copy(uniques, name=None)
elif isinstance(values, Series):
uniques = Index(uniques)
return labels, uniques
def value_counts(values, sort=True, ascending=False, normalize=False,
bins=None, dropna=True):
"""
Compute a histogram of the counts of non-null values.
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
normalize: boolean, default False
If True then compute a relative histogram
bins : integer, optional
Rather than count values, group them into half-open bins,
convenience for pd.cut, only works with numeric data
dropna : boolean, default True
Don't include counts of NaN
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
from pandas.tools.tile import cut
from pandas import Index, PeriodIndex, DatetimeIndex
name = getattr(values, 'name', None)
values = Series(values).values
if bins is not None:
try:
cat, bins = cut(values, bins, retbins=True)
except TypeError:
raise TypeError("bins argument only works with numeric data.")
values = cat.codes
if com.is_categorical_dtype(values.dtype):
result = values.value_counts(dropna)
else:
dtype = values.dtype
is_period = com.is_period_arraylike(values)
is_datetimetz = com.is_datetimetz(values)
if com.is_datetime_or_timedelta_dtype(dtype) or is_period or \
is_datetimetz:
if is_period:
values = PeriodIndex(values)
elif is_datetimetz:
tz = getattr(values, 'tz', None)
values = DatetimeIndex(values).tz_localize(None)
values = values.view(np.int64)
keys, counts = htable.value_count_scalar64(values, dropna)
if dropna:
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
# localize to the original tz if necessary
if is_datetimetz:
keys = DatetimeIndex(keys).tz_localize(tz)
# convert the keys back to the dtype we came in
else:
keys = keys.astype(dtype)
elif com.is_integer_dtype(dtype):
values = com._ensure_int64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
elif com.is_float_dtype(dtype):
values = com._ensure_float64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
else:
values = com._ensure_object(values)
mask = com.isnull(values)
keys, counts = htable.value_count_object(values, mask)
if not dropna and mask.any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
if not isinstance(keys, Index):
keys = Index(keys)
result = Series(counts, index=keys, name=name)
if bins is not None:
# TODO: This next line should be more efficient
result = result.reindex(np.arange(len(cat.categories)),
fill_value=0)
result.index = bins[:-1]
if sort:
result = result.sort_values(ascending=ascending)
if normalize:
result = result / float(values.size)
return result
def test_astype_raises(self, dtype):
# GH 13149, GH 13209
idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN])
msg = "Cannot cast DatetimeArray to dtype"
with pytest.raises(TypeError, match=msg):
idx.astype(dtype)
