class Take(object):
params = ['int', 'datetime']
param_names = ['index']
def setup(self, index):
N = 100000
indexes = {'int': Int64Index(np.arange(N)),
'datetime': date_range('2011-01-01', freq='S', periods=N)}
index = indexes[index]
self.s = Series(np.random.rand(N), index=index)
self.indexer = [True, False, True, True, False] * 20000
def time_take(self, index):
self.s.take(self.indexer)
class Take:
params = ['int', 'datetime']
param_names = ['index']
def setup(self, index):
N = 100000
indexes = {'int': Int64Index(np.arange(N)),
'datetime': date_range('2011-01-01', freq='S', periods=N)}
index = indexes[index]
self.s = Series(np.random.rand(N), index=index)
self.indexer = [True, False, True, True, False] * 20000
def time_take(self, index):
self.s.take(self.indexer)
def test_depr_take_kwarg_is_copy(self, is_copy):
# GH 27357
df = DataFrame({"A": [1, 2, 3]})
msg = (
"is_copy is deprecated and will be removed in a future version. "
"'take' always returns a copy, so there is no need to specify this."
)
with tm.assert_produces_warning(FutureWarning) as w:
df.take([0, 1], is_copy=is_copy)
assert w[0].message.args[0] == msg
s = Series([1, 2, 3])
with tm.assert_produces_warning(FutureWarning):
s.take([0, 1], is_copy=is_copy)
def test_take():
s = Series([-1, 5, 6, 2, 4])
actual = s.take([1, 3, 4])
expected = Series([5, 2, 4], index=[1, 3, 4])
tm.assert_series_equal(actual, expected)
actual = s.take([-1, 3, 4])
expected = Series([4, 2, 4], index=[4, 3, 4])
tm.assert_series_equal(actual, expected)
pytest.raises(IndexError, s.take, [1, 10])
pytest.raises(IndexError, s.take, [2, 5])
with tm.assert_produces_warning(FutureWarning):
s.take([-1, 3, 4], convert=False)
def test_take(self):
s = Series([-1, 5, 6, 2, 4])
actual = s.take([1, 3, 4])
expected = Series([5, 2, 4], index=[1, 3, 4])
tm.assert_series_equal(actual, expected)
actual = s.take([-1, 3, 4])
expected = Series([4, 2, 4], index=[4, 3, 4])
tm.assert_series_equal(actual, expected)
pytest.raises(IndexError, s.take, [1, 10])
pytest.raises(IndexError, s.take, [2, 5])
with tm.assert_produces_warning(FutureWarning):
s.take([-1, 3, 4], convert=False)
def test_take_categorical():
# https://github.com/pandas-dev/pandas/issues/20664
s = Series(pd.Categorical(["a", "b", "c"]))
result = s.take([-2, -2, 0])
expected = Series(pd.Categorical(["b", "b", "a"],
categories=["a", "b", "c"]),
index=[1, 1, 0])
tm.assert_series_equal(result, expected)
def test_take_categorical():
# https://github.com/pandas-dev/pandas/issues/20664
s = Series(pd.Categorical(['a', 'b', 'c']))
result = s.take([-2, -2, 0])
expected = Series(pd.Categorical(['b', 'b', 'a'],
categories=['a', 'b', 'c']),
index=[1, 1, 0])
assert_series_equal(result, expected)
def test_take_categorical():
# https://github.com/pandas-dev/pandas/issues/20664
s = Series(pd.Categorical(['a', 'b', 'c']))
result = s.take([-2, -2, 0])
expected = Series(pd.Categorical(['b', 'b', 'a'],
categories=['a', 'b', 'c']),
index=[1, 1, 0])
assert_series_equal(result, expected)
class Take:
params = ["int", "datetime"]
param_names = ["index"]
def setup(self, index):
N = 100000
indexes = {
"int": Int64Index(np.arange(N)),
"datetime": date_range("2011-01-01", freq="S", periods=N),
}
index = indexes[index]
self.s = Series(np.random.rand(N), index=index)
self.indexer = [True, False, True, True, False] * 20000
def time_take(self, index):
self.s.take(self.indexer)
def test_resample_not_monotonic(self):
rng = pd.date_range('2012-06-12', periods=200, freq='h')
ts = Series(np.random.randn(len(rng)), index=rng)
ts = ts.take(np.random.permutation(len(ts)))
result = ts.resample('D', how='sum')
exp = ts.sort_index().resample('D', how='sum')
assert_series_equal(result, exp)
def test_resample_not_monotonic():
rng = pd.date_range("2012-06-12", periods=200, freq="h")
ts = Series(np.random.randn(len(rng)), index=rng)
ts = ts.take(np.random.permutation(len(ts)))
result = ts.resample("D").sum()
exp = ts.sort_index().resample("D").sum()
tm.assert_series_equal(result, exp)
def test_getitem_slice_date(self, slc, positions):
# https://github.com/pandas-dev/pandas/issues/31501
ser = Series(
[0, 1, 2],
DatetimeIndex(["2019-01-01", "2019-01-01T06:00:00", "2019-01-02"]),
)
result = ser[slc]
expected = ser.take(positions)
tm.assert_series_equal(result, expected)
def test_resample_not_monotonic(self):
rng = pd.date_range('2012-06-12', periods=200, freq='h')
ts = Series(np.random.randn(len(rng)), index=rng)
ts = ts.take(np.random.permutation(len(ts)))
result = ts.resample('D', how='sum')
exp = ts.sort_index().resample('D', how='sum')
assert_series_equal(result, exp)
def test_indexing_unordered2():
# diff freq
rng = date_range(datetime(2005, 1, 1), periods=20, freq="M")
ts = Series(np.arange(len(rng)), index=rng)
ts = ts.take(np.random.permutation(20))
result = ts["2005"]
for t in result.index:
assert t.year == 2005
def test_take():
s = Series([-1, 5, 6, 2, 4])
actual = s.take([1, 3, 4])
expected = Series([5, 2, 4], index=[1, 3, 4])
tm.assert_series_equal(actual, expected)
actual = s.take([-1, 3, 4])
expected = Series([4, 2, 4], index=[4, 3, 4])
tm.assert_series_equal(actual, expected)
msg = "index {} is out of bounds for size 5"
with pytest.raises(IndexError, match=msg.format(10)):
s.take([1, 10])
with pytest.raises(IndexError, match=msg.format(5)):
s.take([2, 5])
with tm.assert_produces_warning(FutureWarning):
s.take([-1, 3, 4], convert=False)
def test_take():
s = Series([-1, 5, 6, 2, 4])
actual = s.take([1, 3, 4])
expected = Series([5, 2, 4], index=[1, 3, 4])
tm.assert_series_equal(actual, expected)
actual = s.take([-1, 3, 4])
expected = Series([4, 2, 4], index=[4, 3, 4])
tm.assert_series_equal(actual, expected)
msg = "index {} is out of bounds for size 5"
with pytest.raises(IndexError, match=msg.format(10)):
s.take([1, 10])
with pytest.raises(IndexError, match=msg.format(5)):
s.take([2, 5])
with tm.assert_produces_warning(FutureWarning):
s.take([-1, 3, 4], convert=False)
def test_indexing_unordered():
# GH 2437
rng = date_range(start="2011-01-01", end="2011-01-15")
ts = Series(np.random.rand(len(rng)), index=rng)
ts2 = pd.concat([ts[0:4], ts[-4:], ts[4:-4]])
for t in ts.index:
# TODO: unused?
s = str(t) # noqa
expected = ts[t]
result = ts2[t]
assert expected == result
# GH 3448 (ranges)
def compare(slobj):
result = ts2[slobj].copy()
result = result.sort_index()
expected = ts[slobj]
expected.index = expected.index._with_freq(None)
tm.assert_series_equal(result, expected)
compare(slice("2011-01-01", "2011-01-15"))
compare(slice("2010-12-30", "2011-01-15"))
compare(slice("2011-01-01", "2011-01-16"))
# partial ranges
compare(slice("2011-01-01", "2011-01-6"))
compare(slice("2011-01-06", "2011-01-8"))
compare(slice("2011-01-06", "2011-01-12"))
# single values
result = ts2["2011"].sort_index()
expected = ts["2011"]
expected.index = expected.index._with_freq(None)
tm.assert_series_equal(result, expected)
# diff freq
rng = date_range(datetime(2005, 1, 1), periods=20, freq="M")
ts = Series(np.arange(len(rng)), index=rng)
ts = ts.take(np.random.permutation(20))
result = ts["2005"]
for t in result.index:
assert t.year == 2005
def test_indexing_unordered():
# GH 2437
rng = date_range(start='2011-01-01', end='2011-01-15')
ts = Series(np.random.rand(len(rng)), index=rng)
ts2 = pd.concat([ts[0:4], ts[-4:], ts[4:-4]])
for t in ts.index:
# TODO: unused?
s = str(t) # noqa
expected = ts[t]
result = ts2[t]
assert expected == result
# GH 3448 (ranges)
def compare(slobj):
result = ts2[slobj].copy()
result = result.sort_index()
expected = ts[slobj]
assert_series_equal(result, expected)
compare(slice('2011-01-01', '2011-01-15'))
compare(slice('2010-12-30', '2011-01-15'))
compare(slice('2011-01-01', '2011-01-16'))
# partial ranges
compare(slice('2011-01-01', '2011-01-6'))
compare(slice('2011-01-06', '2011-01-8'))
compare(slice('2011-01-06', '2011-01-12'))
# single values
result = ts2['2011'].sort_index()
expected = ts['2011']
assert_series_equal(result, expected)
# diff freq
rng = date_range(datetime(2005, 1, 1), periods=20, freq='M')
ts = Series(np.arange(len(rng)), index=rng)
ts = ts.take(np.random.permutation(20))
result = ts['2005']
for t in result.index:
assert t.year == 2005
def test_indexing_unordered(self):
# GH 2437
rng = date_range(start='2011-01-01', end='2011-01-15')
ts = Series(np.random.rand(len(rng)), index=rng)
ts2 = pd.concat([ts[0:4], ts[-4:], ts[4:-4]])
for t in ts.index:
# TODO: unused?
s = str(t) # noqa
expected = ts[t]
result = ts2[t]
assert expected == result
# GH 3448 (ranges)
def compare(slobj):
result = ts2[slobj].copy()
result = result.sort_index()
expected = ts[slobj]
assert_series_equal(result, expected)
compare(slice('2011-01-01', '2011-01-15'))
compare(slice('2010-12-30', '2011-01-15'))
compare(slice('2011-01-01', '2011-01-16'))
# partial ranges
compare(slice('2011-01-01', '2011-01-6'))
compare(slice('2011-01-06', '2011-01-8'))
compare(slice('2011-01-06', '2011-01-12'))
# single values
result = ts2['2011'].sort_index()
expected = ts['2011']
assert_series_equal(result, expected)
# diff freq
rng = date_range(datetime(2005, 1, 1), periods=20, freq='M')
ts = Series(np.arange(len(rng)), index=rng)
ts = ts.take(np.random.permutation(20))
result = ts['2005']
for t in result.index:
assert t.year == 2005
def test_legacy_time_rule_arg(self):
# suppress deprecation warnings
sys.stderr = StringIO()
rng = bdate_range('1/1/2000', periods=20)
ts = Series(np.random.randn(20), index=rng)
ts = ts.take(np.random.permutation(len(ts))[:12]).sort_index()
try:
result = mom.rolling_mean(ts, 1, min_periods=1, freq='B')
expected = mom.rolling_mean(ts, 1, min_periods=1,
time_rule='WEEKDAY')
tm.assert_series_equal(result, expected)
result = mom.ewma(ts, span=5, freq='B')
expected = mom.ewma(ts, span=5, time_rule='WEEKDAY')
tm.assert_series_equal(result, expected)
finally:
sys.stderr = sys.__stderr__
def test_legacy_time_rule_arg(self):
# suppress deprecation warnings
sys.stderr = StringIO()
rng = bdate_range('1/1/2000', periods=20)
ts = Series(np.random.randn(20), index=rng)
ts = ts.take(np.random.permutation(len(ts))[:12]).sort_index()
try:
result = mom.rolling_mean(ts, 1, min_periods=1, freq='B')
expected = mom.rolling_mean(ts, 1, min_periods=1,
time_rule='WEEKDAY')
tm.assert_series_equal(result, expected)
result = mom.ewma(ts, span=5, freq='B')
expected = mom.ewma(ts, span=5, time_rule='WEEKDAY')
tm.assert_series_equal(result, expected)
finally:
sys.stderr = sys.__stderr__
def test_take():
s = Series([-1, 5, 6, 2, 4])
actual = s.take([1, 3, 4])
expected = Series([5, 2, 4], index=[1, 3, 4])
tm.assert_series_equal(actual, expected)
actual = s.take([-1, 3, 4])
expected = Series([4, 2, 4], index=[4, 3, 4])
tm.assert_series_equal(actual, expected)
msg = "index {} is out of bounds for( axis 0 with)? size 5"
with pytest.raises(IndexError, match=msg.format(10)):
s.take([1, 10])
with pytest.raises(IndexError, match=msg.format(5)):
s.take([2, 5])
def test_partial_not_monotonic(self):
rng = date_range(datetime(2005, 1, 1), periods=20, freq="M")
ts = Series(np.arange(len(rng)), index=rng)
ts = ts.take(np.random.permutation(20))
self.assertRaises(Exception, ts.__getitem__, "2005")
# In[296]: rng[0] # In[297]: rng[1] ##### observe how the date is increasing step by step ##### Notice that the offset is D so the data is increasing day by day # In[298]: ts2 = ts.take(np.random.permutation(len(ts))) # In[299]: ts2 # In[300]: ts2.sort_index() ##### sort_index returns a time series sorted by date # In[301]: ts['2012-7-26']
rng[0] # In[297]: rng[1] ##### observe how the date is increasing step by step ##### Notice that the offset is D so the data is increasing day by day # In[298]: ts2=ts.take(np.random.permutation(len(ts))) # In[299]: ts2 # In[300]: ts2.sort_index() ##### sort_index returns a time series sorted by date # In[301]:
