def test_quantile_multi(self):
qs = [.1, .9]
result = self.ts.quantile(qs)
expected = pd.Series([np.percentile(self.ts.dropna(), 10),
np.percentile(self.ts.dropna(), 90)],
index=qs, name=self.ts.name)
tm.assert_series_equal(result, expected)
dts = self.ts.index.to_series()
dts.name = 'xxx'
result = dts.quantile((.2, .2))
expected = Series([Timestamp('2000-01-10 19:12:00'),
Timestamp('2000-01-10 19:12:00')],
index=[.2, .2], name='xxx')
tm.assert_series_equal(result, expected)
result = self.ts.quantile([])
expected = pd.Series([], name=self.ts.name, index=Index(
[], dtype=float))
tm.assert_series_equal(result, expected)
def test_operators_datetimelike(self):
# ## timedelta64 ###
td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
td1.iloc[2] = np.nan
# ## datetime64 ###
dt1 = Series([
Timestamp('20111230'),
Timestamp('20120101'),
Timestamp('20120103')
])
dt1.iloc[2] = np.nan
dt2 = Series([
Timestamp('20111231'),
Timestamp('20120102'),
Timestamp('20120104')
])
dt1 - dt2
dt2 - dt1
# ## datetime64 with timetimedelta ###
dt1 + td1
td1 + dt1
dt1 - td1
# TODO: Decide if this ought to work.
# td1 - dt1
# ## timetimedelta with datetime64 ###
td1 + dt1
dt1 + td1
def test_operators_datetimelike_invalid(self, all_arithmetic_operators):
# these are all TypeEror ops
op_str = all_arithmetic_operators
def check(get_ser, test_ser):
# check that we are getting a TypeError
# with 'operate' (from core/ops.py) for the ops that are not
# defined
op = getattr(get_ser, op_str, None)
with tm.assert_raises_regex(TypeError, 'operate|cannot'):
op(test_ser)
# ## timedelta64 ###
td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
td1.iloc[2] = np.nan
# ## datetime64 ###
dt1 = Series([
Timestamp('20111230'),
Timestamp('20120101'),
Timestamp('20120103')
])
dt1.iloc[2] = np.nan
dt2 = Series([
Timestamp('20111231'),
Timestamp('20120102'),
Timestamp('20120104')
])
if op_str not in ['__sub__', '__rsub__']:
check(dt1, dt2)
# ## datetime64 with timetimedelta ###
# TODO(jreback) __rsub__ should raise?
if op_str not in ['__add__', '__radd__', '__sub__']:
check(dt1, td1)
# 8260, 10763
# datetime64 with tz
tz = 'US/Eastern'
dt1 = Series(date_range('2000-01-01 09:00:00', periods=5, tz=tz),
name='foo')
dt2 = dt1.copy()
dt2.iloc[2] = np.nan
td1 = Series(timedelta_range('1 days 1 min', periods=5, freq='H'))
td2 = td1.copy()
td2.iloc[1] = np.nan
if op_str not in ['__add__', '__radd__', '__sub__', '__rsub__']:
check(dt2, td2)
def test_constructor_dict_datetime64_index(self):
# GH 9456
dates_as_str = ['1984-02-19', '1988-11-06', '1989-12-03', '1990-03-15']
values = [42544017.198965244, 1234565, 40512335.181958228, -1]
def create_data(constructor):
return dict(zip((constructor(x) for x in dates_as_str), values))
data_datetime64 = create_data(np.datetime64)
data_datetime = create_data(lambda x: datetime.strptime(x, '%Y-%m-%d'))
data_Timestamp = create_data(Timestamp)
expected = Series(values, (Timestamp(x) for x in dates_as_str))
result_datetime64 = Series(data_datetime64)
result_datetime = Series(data_datetime)
result_Timestamp = Series(data_Timestamp)
assert_series_equal(result_datetime64, expected)
assert_series_equal(result_datetime, expected)
assert_series_equal(result_Timestamp, expected)
def test_convert_objects(self):
s = Series([1., 2, 3], index=['a', 'b', 'c'])
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_dates=False,
convert_numeric=True)
assert_series_equal(result, s)
# force numeric conversion
r = s.copy().astype('O')
r['a'] = '1'
with tm.assert_produces_warning(FutureWarning):
result = r.convert_objects(convert_dates=False,
convert_numeric=True)
assert_series_equal(result, s)
r = s.copy().astype('O')
r['a'] = '1.'
with tm.assert_produces_warning(FutureWarning):
result = r.convert_objects(convert_dates=False,
convert_numeric=True)
assert_series_equal(result, s)
r = s.copy().astype('O')
r['a'] = 'garbled'
expected = s.copy()
expected['a'] = np.nan
with tm.assert_produces_warning(FutureWarning):
result = r.convert_objects(convert_dates=False,
convert_numeric=True)
assert_series_equal(result, expected)
# GH 4119, not converting a mixed type (e.g.floats and object)
s = Series([1, 'na', 3, 4])
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_numeric=True)
expected = Series([1, np.nan, 3, 4])
assert_series_equal(result, expected)
s = Series([1, '', 3, 4])
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_numeric=True)
expected = Series([1, np.nan, 3, 4])
assert_series_equal(result, expected)
# dates
s = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 2, 0, 0),
datetime(2001, 1, 3, 0, 0)])
s2 = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 2, 0, 0),
datetime(2001, 1, 3, 0, 0), 'foo', 1.0, 1,
Timestamp('20010104'), '20010105'],
dtype='O')
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_dates=True,
convert_numeric=False)
expected = Series([Timestamp('20010101'), Timestamp('20010102'),
Timestamp('20010103')], dtype='M8[ns]')
assert_series_equal(result, expected)
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_dates='coerce',
convert_numeric=False)
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_dates='coerce',
convert_numeric=True)
assert_series_equal(result, expected)
expected = Series([Timestamp('20010101'), Timestamp('20010102'),
Timestamp('20010103'),
lib.NaT, lib.NaT, lib.NaT, Timestamp('20010104'),
Timestamp('20010105')], dtype='M8[ns]')
with tm.assert_produces_warning(FutureWarning):
result = s2.convert_objects(convert_dates='coerce',
convert_numeric=False)
assert_series_equal(result, expected)
with tm.assert_produces_warning(FutureWarning):
result = s2.convert_objects(convert_dates='coerce',
convert_numeric=True)
assert_series_equal(result, expected)
# preserver all-nans (if convert_dates='coerce')
s = Series(['foo', 'bar', 1, 1.0], dtype='O')
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_dates='coerce',
convert_numeric=False)
expected = Series([lib.NaT] * 2 + [Timestamp(1)] * 2)
assert_series_equal(result, expected)
# preserver if non-object
s = Series([1], dtype='float32')
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_dates='coerce',
convert_numeric=False)
assert_series_equal(result, s)
# r = s.copy()
# r[0] = np.nan
# result = r.convert_objects(convert_dates=True,convert_numeric=False)
# assert result.dtype == 'M8[ns]'
# dateutil parses some single letters into today's value as a date
for x in 'abcdefghijklmnopqrstuvwxyz':
s = Series([x])
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_dates='coerce')
assert_series_equal(result, s)
s = Series([x.upper()])
with tm.assert_produces_warning(FutureWarning):
result = s.convert_objects(convert_dates='coerce')
assert_series_equal(result, s)
def test_convert(self):
# Tests: All to nans, coerce, true
# Test coercion returns correct type
s = Series(['a', 'b', 'c'])
results = s._convert(datetime=True, coerce=True)
expected = Series([lib.NaT] * 3)
assert_series_equal(results, expected)
results = s._convert(numeric=True, coerce=True)
expected = Series([np.nan] * 3)
assert_series_equal(results, expected)
expected = Series([lib.NaT] * 3, dtype=np.dtype('m8[ns]'))
results = s._convert(timedelta=True, coerce=True)
assert_series_equal(results, expected)
dt = datetime(2001, 1, 1, 0, 0)
td = dt - datetime(2000, 1, 1, 0, 0)
# Test coercion with mixed types
s = Series(['a', '3.1415', dt, td])
results = s._convert(datetime=True, coerce=True)
expected = Series([lib.NaT, lib.NaT, dt, lib.NaT])
assert_series_equal(results, expected)
results = s._convert(numeric=True, coerce=True)
expected = Series([nan, 3.1415, nan, nan])
assert_series_equal(results, expected)
results = s._convert(timedelta=True, coerce=True)
expected = Series([lib.NaT, lib.NaT, lib.NaT, td],
dtype=np.dtype('m8[ns]'))
assert_series_equal(results, expected)
# Test standard conversion returns original
results = s._convert(datetime=True)
assert_series_equal(results, s)
results = s._convert(numeric=True)
expected = Series([nan, 3.1415, nan, nan])
assert_series_equal(results, expected)
results = s._convert(timedelta=True)
assert_series_equal(results, s)
# test pass-through and non-conversion when other types selected
s = Series(['1.0', '2.0', '3.0'])
results = s._convert(datetime=True, numeric=True, timedelta=True)
expected = Series([1.0, 2.0, 3.0])
assert_series_equal(results, expected)
results = s._convert(True, False, True)
assert_series_equal(results, s)
s = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 1, 0, 0)],
dtype='O')
results = s._convert(datetime=True, numeric=True, timedelta=True)
expected = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 1, 0,
0)])
assert_series_equal(results, expected)
results = s._convert(datetime=False, numeric=True, timedelta=True)
assert_series_equal(results, s)
td = datetime(2001, 1, 1, 0, 0) - datetime(2000, 1, 1, 0, 0)
s = Series([td, td], dtype='O')
results = s._convert(datetime=True, numeric=True, timedelta=True)
expected = Series([td, td])
assert_series_equal(results, expected)
results = s._convert(True, True, False)
assert_series_equal(results, s)
s = Series([1., 2, 3], index=['a', 'b', 'c'])
result = s._convert(numeric=True)
assert_series_equal(result, s)
# force numeric conversion
r = s.copy().astype('O')
r['a'] = '1'
result = r._convert(numeric=True)
assert_series_equal(result, s)
r = s.copy().astype('O')
r['a'] = '1.'
result = r._convert(numeric=True)
assert_series_equal(result, s)
r = s.copy().astype('O')
r['a'] = 'garbled'
result = r._convert(numeric=True)
expected = s.copy()
expected['a'] = nan
assert_series_equal(result, expected)
# GH 4119, not converting a mixed type (e.g.floats and object)
s = Series([1, 'na', 3, 4])
result = s._convert(datetime=True, numeric=True)
expected = Series([1, nan, 3, 4])
assert_series_equal(result, expected)
s = Series([1, '', 3, 4])
result = s._convert(datetime=True, numeric=True)
assert_series_equal(result, expected)
# dates
s = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 2, 0, 0),
datetime(2001, 1, 3, 0, 0)])
s2 = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 2, 0, 0),
datetime(2001, 1, 3, 0, 0), 'foo', 1.0, 1,
Timestamp('20010104'), '20010105'], dtype='O')
result = s._convert(datetime=True)
expected = Series([Timestamp('20010101'), Timestamp('20010102'),
Timestamp('20010103')], dtype='M8[ns]')
assert_series_equal(result, expected)
result = s._convert(datetime=True, coerce=True)
assert_series_equal(result, expected)
expected = Series([Timestamp('20010101'), Timestamp('20010102'),
Timestamp('20010103'), lib.NaT, lib.NaT, lib.NaT,
Timestamp('20010104'), Timestamp('20010105')],
dtype='M8[ns]')
result = s2._convert(datetime=True, numeric=False, timedelta=False,
coerce=True)
assert_series_equal(result, expected)
result = s2._convert(datetime=True, coerce=True)
assert_series_equal(result, expected)
s = Series(['foo', 'bar', 1, 1.0], dtype='O')
result = s._convert(datetime=True, coerce=True)
expected = Series([lib.NaT] * 2 + [Timestamp(1)] * 2)
assert_series_equal(result, expected)
# preserver if non-object
s = Series([1], dtype='float32')
result = s._convert(datetime=True, coerce=True)
assert_series_equal(result, s)
# r = s.copy()
# r[0] = np.nan
# result = r._convert(convert_dates=True,convert_numeric=False)
# assert result.dtype == 'M8[ns]'
# dateutil parses some single letters into today's value as a date
expected = Series([lib.NaT])
for x in 'abcdefghijklmnopqrstuvwxyz':
s = Series([x])
result = s._convert(datetime=True, coerce=True)
assert_series_equal(result, expected)
s = Series([x.upper()])
result = s._convert(datetime=True, coerce=True)
assert_series_equal(result, expected)
def test_categorical_delegations(self):
# invalid accessor
pytest.raises(AttributeError, lambda: Series([1, 2, 3]).cat)
tm.assert_raises_regex(
AttributeError,
r"Can only use .cat accessor with a 'category' dtype",
lambda: Series([1, 2, 3]).cat)
pytest.raises(AttributeError, lambda: Series(['a', 'b', 'c']).cat)
pytest.raises(AttributeError, lambda: Series(np.arange(5.)).cat)
pytest.raises(AttributeError,
lambda: Series([Timestamp('20130101')]).cat)
# Series should delegate calls to '.categories', '.codes', '.ordered'
# and the methods '.set_categories()' 'drop_unused_categories()' to the
# categorical# -*- coding: utf-8 -*-
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
exp_categories = Index(["a", "b", "c"])
tm.assert_index_equal(s.cat.categories, exp_categories)
s.cat.categories = [1, 2, 3]
exp_categories = Index([1, 2, 3])
tm.assert_index_equal(s.cat.categories, exp_categories)
exp_codes = Series([0, 1, 2, 0], dtype='int8')
tm.assert_series_equal(s.cat.codes, exp_codes)
assert s.cat.ordered
s = s.cat.as_unordered()
assert not s.cat.ordered
s.cat.as_ordered(inplace=True)
assert s.cat.ordered
# reorder
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
exp_categories = Index(["c", "b", "a"])
exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_)
s = s.cat.set_categories(["c", "b", "a"])
tm.assert_index_equal(s.cat.categories, exp_categories)
tm.assert_numpy_array_equal(s.values.__array__(), exp_values)
tm.assert_numpy_array_equal(s.__array__(), exp_values)
# remove unused categories
s = Series(
Categorical(["a", "b", "b", "a"], categories=["a", "b", "c"]))
exp_categories = Index(["a", "b"])
exp_values = np.array(["a", "b", "b", "a"], dtype=np.object_)
s = s.cat.remove_unused_categories()
tm.assert_index_equal(s.cat.categories, exp_categories)
tm.assert_numpy_array_equal(s.values.__array__(), exp_values)
tm.assert_numpy_array_equal(s.__array__(), exp_values)
# This method is likely to be confused, so test that it raises an error
# on wrong inputs:
def f():
s.set_categories([4, 3, 2, 1])
pytest.raises(Exception, f)
# right: s.cat.set_categories([4,3,2,1])
# GH18862 (let Series.cat.rename_categories take callables)
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
result = s.cat.rename_categories(lambda x: x.upper())
expected = Series(
Categorical(["A", "B", "C", "A"],
categories=["A", "B", "C"],
ordered=True))
tm.assert_series_equal(result, expected)
def test_constructor_with_datetime_tz(self):
# 8260
# support datetime64 with tz
dr = date_range('20130101', periods=3, tz='US/Eastern')
s = Series(dr)
assert s.dtype.name == 'datetime64[ns, US/Eastern]'
assert s.dtype == 'datetime64[ns, US/Eastern]'
assert is_datetime64tz_dtype(s.dtype)
assert 'datetime64[ns, US/Eastern]' in str(s)
# export
result = s.values
assert isinstance(result, np.ndarray)
assert result.dtype == 'datetime64[ns]'
exp = pd.DatetimeIndex(result)
exp = exp.tz_localize('UTC').tz_convert(tz=s.dt.tz)
tm.assert_index_equal(dr, exp)
# indexing
result = s.iloc[0]
self.assertEqual(
result,
Timestamp('2013-01-01 00:00:00-0500', tz='US/Eastern', freq='D'))
result = s[0]
self.assertEqual(
result,
Timestamp('2013-01-01 00:00:00-0500', tz='US/Eastern', freq='D'))
result = s[Series([True, True, False], index=s.index)]
assert_series_equal(result, s[0:2])
result = s.iloc[0:1]
assert_series_equal(result, Series(dr[0:1]))
# concat
result = pd.concat([s.iloc[0:1], s.iloc[1:]])
assert_series_equal(result, s)
# astype
result = s.astype(object)
expected = Series(DatetimeIndex(s._values).asobject)
assert_series_equal(result, expected)
result = Series(s.values).dt.tz_localize('UTC').dt.tz_convert(s.dt.tz)
assert_series_equal(result, s)
# astype - datetime64[ns, tz]
result = Series(s.values).astype('datetime64[ns, US/Eastern]')
assert_series_equal(result, s)
result = Series(s.values).astype(s.dtype)
assert_series_equal(result, s)
result = s.astype('datetime64[ns, CET]')
expected = Series(date_range('20130101 06:00:00', periods=3, tz='CET'))
assert_series_equal(result, expected)
# short str
assert 'datetime64[ns, US/Eastern]' in str(s)
# formatting with NaT
result = s.shift()
assert 'datetime64[ns, US/Eastern]' in str(result)
assert 'NaT' in str(result)
# long str
t = Series(date_range('20130101', periods=1000, tz='US/Eastern'))
assert 'datetime64[ns, US/Eastern]' in str(t)
result = pd.DatetimeIndex(s, freq='infer')
tm.assert_index_equal(result, dr)
# inference
s = Series([
pd.Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific'),
pd.Timestamp('2013-01-02 14:00:00-0800', tz='US/Pacific')
])
assert s.dtype == 'datetime64[ns, US/Pacific]'
assert lib.infer_dtype(s) == 'datetime64'
s = Series([
pd.Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific'),
pd.Timestamp('2013-01-02 14:00:00-0800', tz='US/Eastern')
])
assert s.dtype == 'object'
assert lib.infer_dtype(s) == 'datetime'
# with all NaT
s = Series(pd.NaT, index=[0, 1], dtype='datetime64[ns, US/Eastern]')
expected = Series(pd.DatetimeIndex(['NaT', 'NaT'], tz='US/Eastern'))
assert_series_equal(s, expected)
def test_constructor_dtype_datetime64(self):
s = Series(iNaT, dtype='M8[ns]', index=lrange(5))
assert isnull(s).all()
# in theory this should be all nulls, but since
# we are not specifying a dtype is ambiguous
s = Series(iNaT, index=lrange(5))
assert not isnull(s).all()
s = Series(nan, dtype='M8[ns]', index=lrange(5))
assert isnull(s).all()
s = Series([datetime(2001, 1, 2, 0, 0), iNaT], dtype='M8[ns]')
assert isnull(s[1])
self.assertEqual(s.dtype, 'M8[ns]')
s = Series([datetime(2001, 1, 2, 0, 0), nan], dtype='M8[ns]')
assert isnull(s[1])
self.assertEqual(s.dtype, 'M8[ns]')
# GH3416
dates = [
np.datetime64(datetime(2013, 1, 1)),
np.datetime64(datetime(2013, 1, 2)),
np.datetime64(datetime(2013, 1, 3)),
]
s = Series(dates)
self.assertEqual(s.dtype, 'M8[ns]')
s.iloc[0] = np.nan
self.assertEqual(s.dtype, 'M8[ns]')
# invalid astypes
for t in ['s', 'D', 'us', 'ms']:
pytest.raises(TypeError, s.astype, 'M8[%s]' % t)
# GH3414 related
pytest.raises(
TypeError, lambda x: Series(Series(dates).astype('int') / 1000000,
dtype='M8[ms]'))
pytest.raises(TypeError, lambda x: Series(dates, dtype='datetime64'))
# invalid dates can be help as object
result = Series([datetime(2, 1, 1)])
self.assertEqual(result[0], datetime(2, 1, 1, 0, 0))
result = Series([datetime(3000, 1, 1)])
self.assertEqual(result[0], datetime(3000, 1, 1, 0, 0))
# don't mix types
result = Series([Timestamp('20130101'), 1], index=['a', 'b'])
self.assertEqual(result['a'], Timestamp('20130101'))
self.assertEqual(result['b'], 1)
# GH6529
# coerce datetime64 non-ns properly
dates = date_range('01-Jan-2015', '01-Dec-2015', freq='M')
values2 = dates.view(np.ndarray).astype('datetime64[ns]')
expected = Series(values2, index=dates)
for dtype in ['s', 'D', 'ms', 'us', 'ns']:
values1 = dates.view(np.ndarray).astype('M8[{0}]'.format(dtype))
result = Series(values1, dates)
assert_series_equal(result, expected)
# GH 13876
# coerce to non-ns to object properly
expected = Series(values2, index=dates, dtype=object)
for dtype in ['s', 'D', 'ms', 'us', 'ns']:
values1 = dates.view(np.ndarray).astype('M8[{0}]'.format(dtype))
result = Series(values1, index=dates, dtype=object)
assert_series_equal(result, expected)
# leave datetime.date alone
dates2 = np.array([d.date() for d in dates.to_pydatetime()],
dtype=object)
series1 = Series(dates2, dates)
tm.assert_numpy_array_equal(series1.values, dates2)
self.assertEqual(series1.dtype, object)
# these will correctly infer a datetime
s = Series([None, pd.NaT, '2013-08-05 15:30:00.000001'])
self.assertEqual(s.dtype, 'datetime64[ns]')
s = Series([np.nan, pd.NaT, '2013-08-05 15:30:00.000001'])
self.assertEqual(s.dtype, 'datetime64[ns]')
s = Series([pd.NaT, None, '2013-08-05 15:30:00.000001'])
self.assertEqual(s.dtype, 'datetime64[ns]')
s = Series([pd.NaT, np.nan, '2013-08-05 15:30:00.000001'])
self.assertEqual(s.dtype, 'datetime64[ns]')
# tz-aware (UTC and other tz's)
# GH 8411
dr = date_range('20130101', periods=3)
assert Series(dr).iloc[0].tz is None
dr = date_range('20130101', periods=3, tz='UTC')
assert str(Series(dr).iloc[0].tz) == 'UTC'
dr = date_range('20130101', periods=3, tz='US/Eastern')
assert str(Series(dr).iloc[0].tz) == 'US/Eastern'
# non-convertible
s = Series([1479596223000, -1479590, pd.NaT])
assert s.dtype == 'object'
assert s[2] is pd.NaT
assert 'NaT' in str(s)
# if we passed a NaT it remains
s = Series([datetime(2010, 1, 1), datetime(2, 1, 1), pd.NaT])
assert s.dtype == 'object'
assert s[2] is pd.NaT
assert 'NaT' in str(s)
# if we passed a nan it remains
s = Series([datetime(2010, 1, 1), datetime(2, 1, 1), np.nan])
assert s.dtype == 'object'
assert s[2] is np.nan
assert 'NaN' in str(s)
def test_categorical_delegations(self):
# invalid accessor
msg = r"Can only use \.cat accessor with a 'category' dtype"
with pytest.raises(AttributeError, match=msg):
Series([1, 2, 3]).cat
with pytest.raises(AttributeError, match=msg):
Series([1, 2, 3]).cat()
with pytest.raises(AttributeError, match=msg):
Series(["a", "b", "c"]).cat
with pytest.raises(AttributeError, match=msg):
Series(np.arange(5.0)).cat
with pytest.raises(AttributeError, match=msg):
Series([Timestamp("20130101")]).cat
# Series should delegate calls to '.categories', '.codes', '.ordered'
# and the methods '.set_categories()' 'drop_unused_categories()' to the
# categorical
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
exp_categories = Index(["a", "b", "c"])
tm.assert_index_equal(s.cat.categories, exp_categories)
s.cat.categories = [1, 2, 3]
exp_categories = Index([1, 2, 3])
tm.assert_index_equal(s.cat.categories, exp_categories)
exp_codes = Series([0, 1, 2, 0], dtype="int8")
tm.assert_series_equal(s.cat.codes, exp_codes)
assert s.cat.ordered
s = s.cat.as_unordered()
assert not s.cat.ordered
s.cat.as_ordered(inplace=True)
assert s.cat.ordered
# reorder
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
exp_categories = Index(["c", "b", "a"])
exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_)
s = s.cat.set_categories(["c", "b", "a"])
tm.assert_index_equal(s.cat.categories, exp_categories)
tm.assert_numpy_array_equal(s.values.__array__(), exp_values)
tm.assert_numpy_array_equal(s.__array__(), exp_values)
# remove unused categories
s = Series(
Categorical(["a", "b", "b", "a"], categories=["a", "b", "c"]))
exp_categories = Index(["a", "b"])
exp_values = np.array(["a", "b", "b", "a"], dtype=np.object_)
s = s.cat.remove_unused_categories()
tm.assert_index_equal(s.cat.categories, exp_categories)
tm.assert_numpy_array_equal(s.values.__array__(), exp_values)
tm.assert_numpy_array_equal(s.__array__(), exp_values)
# This method is likely to be confused, so test that it raises an error
# on wrong inputs:
msg = "'Series' object has no attribute 'set_categories'"
with pytest.raises(AttributeError, match=msg):
s.set_categories([4, 3, 2, 1])
# right: s.cat.set_categories([4,3,2,1])
# GH18862 (let Series.cat.rename_categories take callables)
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
result = s.cat.rename_categories(lambda x: x.upper())
expected = Series(
Categorical(["A", "B", "C", "A"],
categories=["A", "B", "C"],
ordered=True))
tm.assert_series_equal(result, expected)
class TestSeriesQuantile:
def test_quantile(self, datetime_series):
q = datetime_series.quantile(0.1)
assert q == np.percentile(datetime_series.dropna(), 10)
q = datetime_series.quantile(0.9)
assert q == np.percentile(datetime_series.dropna(), 90)
# object dtype
q = Series(datetime_series, dtype=object).quantile(0.9)
assert q == np.percentile(datetime_series.dropna(), 90)
# datetime64[ns] dtype
dts = datetime_series.index.to_series()
q = dts.quantile(0.2)
assert q == Timestamp("2000-01-10 19:12:00")
# timedelta64[ns] dtype
tds = dts.diff()
q = tds.quantile(0.25)
assert q == pd.to_timedelta("24:00:00")
# GH7661
result = Series([np.timedelta64("NaT")]).sum()
assert result == pd.Timedelta(0)
msg = "percentiles should all be in the interval \\[0, 1\\]"
for invalid in [-1, 2, [0.5, -1], [0.5, 2]]:
with pytest.raises(ValueError, match=msg):
datetime_series.quantile(invalid)
def test_quantile_multi(self, datetime_series):
qs = [0.1, 0.9]
result = datetime_series.quantile(qs)
expected = Series(
[
np.percentile(datetime_series.dropna(), 10),
np.percentile(datetime_series.dropna(), 90),
],
index=qs,
name=datetime_series.name,
)
tm.assert_series_equal(result, expected)
dts = datetime_series.index.to_series()
dts.name = "xxx"
result = dts.quantile((0.2, 0.2))
expected = Series(
[Timestamp("2000-01-10 19:12:00"), Timestamp("2000-01-10 19:12:00")],
index=[0.2, 0.2],
name="xxx",
)
tm.assert_series_equal(result, expected)
result = datetime_series.quantile([])
expected = Series(
[], name=datetime_series.name, index=Index([], dtype=float), dtype="float64"
)
tm.assert_series_equal(result, expected)
def test_quantile_interpolation(self, datetime_series):
# see gh-10174
# interpolation = linear (default case)
q = datetime_series.quantile(0.1, interpolation="linear")
assert q == np.percentile(datetime_series.dropna(), 10)
q1 = datetime_series.quantile(0.1)
assert q1 == np.percentile(datetime_series.dropna(), 10)
# test with and without interpolation keyword
assert q == q1
def test_quantile_interpolation_dtype(self):
# GH #10174
# interpolation = linear (default case)
q = Series([1, 3, 4]).quantile(0.5, interpolation="lower")
assert q == np.percentile(np.array([1, 3, 4]), 50)
assert is_integer(q)
q = Series([1, 3, 4]).quantile(0.5, interpolation="higher")
assert q == np.percentile(np.array([1, 3, 4]), 50)
assert is_integer(q)
def test_quantile_nan(self):
# GH 13098
s = Series([1, 2, 3, 4, np.nan])
result = s.quantile(0.5)
expected = 2.5
assert result == expected
# all nan/empty
s1 = Series([], dtype=object)
cases = [s1, Series([np.nan, np.nan])]
for s in cases:
res = s.quantile(0.5)
assert np.isnan(res)
res = s.quantile([0.5])
tm.assert_series_equal(res, Series([np.nan], index=[0.5]))
res = s.quantile([0.2, 0.3])
tm.assert_series_equal(res, Series([np.nan, np.nan], index=[0.2, 0.3]))
@pytest.mark.parametrize(
"case",
[
[
Timestamp("2011-01-01"),
Timestamp("2011-01-02"),
Timestamp("2011-01-03"),
],
[
Timestamp("2011-01-01", tz="US/Eastern"),
Timestamp("2011-01-02", tz="US/Eastern"),
Timestamp("2011-01-03", tz="US/Eastern"),
],
[pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.Timedelta("3 days")],
# NaT
[
Timestamp("2011-01-01"),
Timestamp("2011-01-02"),
Timestamp("2011-01-03"),
pd.NaT,
],
[
Timestamp("2011-01-01", tz="US/Eastern"),
Timestamp("2011-01-02", tz="US/Eastern"),
Timestamp("2011-01-03", tz="US/Eastern"),
pd.NaT,
],
[
pd.Timedelta("1 days"),
pd.Timedelta("2 days"),
pd.Timedelta("3 days"),
pd.NaT,
],
],
)
def test_quantile_box(self, case):
s = Series(case, name="XXX")
res = s.quantile(0.5)
assert res == case[1]
res = s.quantile([0.5])
exp = Series([case[1]], index=[0.5], name="XXX")
tm.assert_series_equal(res, exp)
def test_datetime_timedelta_quantiles(self):
# covers #9694
assert pd.isna(Series([], dtype="M8[ns]").quantile(0.5))
assert pd.isna(Series([], dtype="m8[ns]").quantile(0.5))
def test_quantile_nat(self):
res = Series([pd.NaT, pd.NaT]).quantile(0.5)
assert res is pd.NaT
res = Series([pd.NaT, pd.NaT]).quantile([0.5])
tm.assert_series_equal(res, Series([pd.NaT], index=[0.5]))
@pytest.mark.parametrize(
"values, dtype",
[([0, 0, 0, 1, 2, 3], "Sparse[int]"), ([0.0, None, 1.0, 2.0], "Sparse[float]")],
)
def test_quantile_sparse(self, values, dtype):
ser = Series(values, dtype=dtype)
result = ser.quantile([0.5])
expected = Series(np.asarray(ser)).quantile([0.5])
tm.assert_series_equal(result, expected)
def test_quantile_empty(self):
# floats
s = Series([], dtype="float64")
res = s.quantile(0.5)
assert np.isnan(res)
res = s.quantile([0.5])
exp = Series([np.nan], index=[0.5])
tm.assert_series_equal(res, exp)
# int
s = Series([], dtype="int64")
res = s.quantile(0.5)
assert np.isnan(res)
res = s.quantile([0.5])
exp = Series([np.nan], index=[0.5])
tm.assert_series_equal(res, exp)
# datetime
s = Series([], dtype="datetime64[ns]")
res = s.quantile(0.5)
assert res is pd.NaT
res = s.quantile([0.5])
exp = Series([pd.NaT], index=[0.5])
tm.assert_series_equal(res, exp)
@pytest.mark.parametrize("dtype", [int, float, "Int64"])
def test_quantile_dtypes(self, dtype):
result = Series([1, 2, 3], dtype=dtype).quantile(np.arange(0, 1, 0.25))
expected = Series(np.arange(1, 3, 0.5), index=np.arange(0, 1, 0.25))
if dtype == "Int64":
expected = expected.astype("Float64")
tm.assert_series_equal(result, expected)
