def test_1d_with_out(self, dtype_can_hold_na, writeable):
dtype, can_hold_na = dtype_can_hold_na
data = np.random.randint(0, 2, 4).astype(dtype)
data.flags.writeable = writeable
indexer = [2, 1, 0, 1]
out = np.empty(4, dtype=dtype)
algos.take_1d(data, indexer, out=out)
expected = data.take(indexer)
tm.assert_almost_equal(out, expected)
indexer = [2, 1, 0, -1]
out = np.empty(4, dtype=dtype)
if can_hold_na:
algos.take_1d(data, indexer, out=out)
expected = data.take(indexer)
expected[3] = np.nan
tm.assert_almost_equal(out, expected)
else:
with pytest.raises(TypeError, match=self.fill_error):
algos.take_1d(data, indexer, out=out)
# No Exception otherwise.
data.take(indexer, out=out)
def test_tz_convert_unsorted(self):
dr = date_range('2012-03-09', freq='H', periods=100, tz='utc')
dr = dr.tz_convert('US/Eastern')
result = dr[::-1].hour
exp = dr.hour[::-1]
tm.assert_almost_equal(result, exp)
def test_all_values_single_bin(self):
# 2070
index = period_range(start="2012-01-01", end="2012-12-31", freq="M")
s = Series(np.random.randn(len(index)), index=index)
result = s.resample("A", how='mean')
tm.assert_almost_equal(result[0], s.mean())
def test_wls_panel(self):
y = tm.makeTimeDataFrame()
x = Panel({'x1' : tm.makeTimeDataFrame(),
'x2' : tm.makeTimeDataFrame()})
y.ix[[1, 7], 'A'] = np.nan
y.ix[[6, 15], 'B'] = np.nan
y.ix[[3, 20], 'C'] = np.nan
y.ix[[5, 11], 'D'] = np.nan
stack_y = y.stack()
stack_x = DataFrame(dict((k, v.stack())
for k, v in x.iteritems()))
weights = x.std('items')
stack_weights = weights.stack()
stack_y.index = stack_y.index.get_tuple_index()
stack_x.index = stack_x.index.get_tuple_index()
stack_weights.index = stack_weights.index.get_tuple_index()
result = ols(y=y, x=x, weights=1/weights)
expected = ols(y=stack_y, x=stack_x, weights=1/stack_weights)
assert_almost_equal(result.beta, expected.beta)
for attr in ['resid', 'y_fitted']:
rvals = getattr(result, attr).stack().values
evals = getattr(expected, attr).values
assert_almost_equal(rvals, evals)
def test_tz_convert_unsorted(self):
dr = date_range("2012-03-09", freq="H", periods=100, tz="utc")
dr = dr.tz_convert(self.tzstr("US/Eastern"))
result = dr[::-1].hour
exp = dr.hour[::-1]
tm.assert_almost_equal(result, exp)
def test_to_records_dict_like(self):
# see gh-18146
class DictLike(object):
def __init__(self, **kwargs):
self.d = kwargs.copy()
def __getitem__(self, key):
return self.d.__getitem__(key)
def __contains__(self, key):
return key in self.d
def keys(self):
return self.d.keys()
df = DataFrame({"A": [1, 2], "B": [0.2, 1.5], "C": ["a", "bc"]})
dtype_mappings = dict(column_dtypes=DictLike(**{"A": np.int8,
"B": np.float32}),
index_dtypes="<U2")
result = df.to_records(**dtype_mappings)
expected = np.rec.array([("0", "1", "0.2", "a"),
("1", "2", "1.5", "bc")],
dtype=[("index", "<U2"), ("A", "i1"),
("B", "<f4"), ("C", "O")])
tm.assert_almost_equal(result, expected)
def test_duplicate_dates_indexing(dups):
ts = dups
uniques = ts.index.unique()
for date in uniques:
result = ts[date]
mask = ts.index == date
total = (ts.index == date).sum()
expected = ts[mask]
if total > 1:
assert_series_equal(result, expected)
else:
assert_almost_equal(result, expected[0])
cp = ts.copy()
cp[date] = 0
expected = Series(np.where(mask, 0, ts), index=ts.index)
assert_series_equal(cp, expected)
pytest.raises(KeyError, ts.__getitem__, datetime(2000, 1, 6))
# new index
ts[datetime(2000, 1, 6)] = 0
assert ts[datetime(2000, 1, 6)] == 0
def test_get_value(self):
for item in self.panel.items:
for mjr in self.panel.major_axis[::2]:
for mnr in self.panel.minor_axis:
result = self.panel.get_value(item, mjr, mnr)
expected = self.panel[item][mnr][mjr]
assert_almost_equal(result, expected)
def testWithTimeEffects(self):
result = ols(y=self.panel_y2, x=self.panel_x2, time_effects=True)
assert_almost_equal(result._y_trans.values.flat, [0, -0.5, 0.5])
exp_x = [[0, 0], [-10.5, -15.5], [10.5, 15.5]]
assert_almost_equal(result._x_trans.values, exp_x)
def test_cat(self):
one = ['a', 'a', 'b', 'b', 'c', NA]
two = ['a', NA, 'b', 'd', 'foo', NA]
# single array
result = strings.str_cat(one)
self.assert_(isnull(result))
result = strings.str_cat(one, na_rep='NA')
exp = 'aabbcNA'
self.assertEquals(result, exp)
result = strings.str_cat(one, na_rep='-')
exp = 'aabbc-'
self.assertEquals(result, exp)
result = strings.str_cat(one, sep='_', na_rep='NA')
exp = 'a_a_b_b_c_NA'
self.assertEquals(result, exp)
# Multiple arrays
result = strings.str_cat(one, [two], na_rep='NA')
exp = ['aa', 'aNA', 'bb', 'bd', 'cfoo', 'NANA']
self.assert_(np.array_equal(result, exp))
result = strings.str_cat(one, two)
exp = ['aa', NA, 'bb', 'bd', 'cfoo', NA]
tm.assert_almost_equal(result, exp)
def test_read_dta18(self):
parsed_118 = self.read_dta(self.dta22_118)
parsed_118["Bytes"] = parsed_118["Bytes"].astype('O')
expected = DataFrame.from_records(
[['Cat', 'Bogota', u'Bogotá', 1, 1.0, u'option b Ünicode', 1.0],
['Dog', 'Boston', u'Uzunköprü', np.nan, np.nan, np.nan, np.nan],
['Plane', 'Rome', u'Tromsø', 0, 0.0, 'option a', 0.0],
['Potato', 'Tokyo', u'Elâzığ', -4, 4.0, 4, 4],
['', '', '', 0, 0.3332999, 'option a', 1/3.]
],
columns=['Things', 'Cities', 'Unicode_Cities_Strl', 'Ints', 'Floats', 'Bytes', 'Longs'])
expected["Floats"] = expected["Floats"].astype(np.float32)
for col in parsed_118.columns:
tm.assert_almost_equal(parsed_118[col], expected[col])
with StataReader(self.dta22_118) as rdr:
vl = rdr.variable_labels()
vl_expected = {u'Unicode_Cities_Strl': u'Here are some strls with Ünicode chars',
u'Longs': u'long data',
u'Things': u'Here are some things',
u'Bytes': u'byte data',
u'Ints': u'int data',
u'Cities': u'Here are some cities',
u'Floats': u'float data'}
tm.assert_dict_equal(vl, vl_expected)
self.assertEqual(rdr.data_label, u'This is a Ünicode data label')
def test_ewma_span_com_args(self):
A = moments.ewma(self.arr, com=9.5)
B = moments.ewma(self.arr, span=20)
assert_almost_equal(A, B)
self.assertRaises(Exception, moments.ewma, self.arr, com=9.5, span=20)
self.assertRaises(Exception, moments.ewma, self.arr)
def test_match(self):
# New match behavior introduced in 0.13
values = Series(['fooBAD__barBAD', NA, 'foo'])
with tm.assert_produces_warning():
result = values.str.match('.*(BAD[_]+).*(BAD)', as_indexer=True)
exp = Series([True, NA, False])
tm.assert_series_equal(result, exp)
# If no groups, use new behavior even when as_indexer is False.
# (Old behavior is pretty much useless in this case.)
values = Series(['fooBAD__barBAD', NA, 'foo'])
result = values.str.match('.*BAD[_]+.*BAD', as_indexer=False)
exp = Series([True, NA, False])
tm.assert_series_equal(result, exp)
# mixed
mixed = Series(['aBAD_BAD', NA, 'BAD_b_BAD', True, datetime.today(),
'foo', None, 1, 2.])
with tm.assert_produces_warning():
rs = Series(mixed).str.match('.*(BAD[_]+).*(BAD)', as_indexer=True)
xp = [True, NA, True, NA, NA, False, NA, NA, NA]
tm.assert_isinstance(rs, Series)
tm.assert_almost_equal(rs, xp)
# unicode
values = Series([u('fooBAD__barBAD'), NA, u('foo')])
with tm.assert_produces_warning():
result = values.str.match('.*(BAD[_]+).*(BAD)', as_indexer=True)
exp = Series([True, NA, False])
tm.assert_series_equal(result, exp)
def test_slice(self):
values = Series(['aafootwo', 'aabartwo', NA, 'aabazqux'])
result = values.str.slice(2, 5)
exp = Series(['foo', 'bar', NA, 'baz'])
tm.assert_series_equal(result, exp)
# mixed
mixed = Series(['aafootwo', NA, 'aabartwo', True, datetime.today(),
None, 1, 2.])
rs = Series(mixed).str.slice(2, 5)
xp = Series(['foo', NA, 'bar', NA, NA,
NA, NA, NA])
tm.assert_isinstance(rs, Series)
tm.assert_almost_equal(rs, xp)
# unicode
values = Series([u('aafootwo'), u('aabartwo'), NA,
u('aabazqux')])
result = values.str.slice(2, 5)
exp = Series([u('foo'), u('bar'), NA, u('baz')])
tm.assert_series_equal(result, exp)
def test_values(self, float_frame, float_string_frame):
frame = float_frame
arr = frame.values
frame_cols = frame.columns
for i, row in enumerate(arr):
for j, value in enumerate(row):
col = frame_cols[j]
if np.isnan(value):
assert np.isnan(frame[col][i])
else:
assert value == frame[col][i]
# mixed type
arr = float_string_frame[['foo', 'A']].values
assert arr[0, 0] == 'bar'
df = self.klass({'complex': [1j, 2j, 3j], 'real': [1, 2, 3]})
arr = df.values
assert arr[0, 0] == 1j
# single block corner case
arr = float_frame[['A', 'B']].values
expected = float_frame.reindex(columns=['A', 'B']).values
assert_almost_equal(arr, expected)
def test_repeat(self):
values = Series(['a', 'b', NA, 'c', NA, 'd'])
result = values.str.repeat(3)
exp = Series(['aaa', 'bbb', NA, 'ccc', NA, 'ddd'])
tm.assert_series_equal(result, exp)
result = values.str.repeat([1, 2, 3, 4, 5, 6])
exp = Series(['a', 'bb', NA, 'cccc', NA, 'dddddd'])
tm.assert_series_equal(result, exp)
# mixed
mixed = Series(['a', NA, 'b', True, datetime.today(), 'foo',
None, 1, 2.])
rs = Series(mixed).str.repeat(3)
xp = ['aaa', NA, 'bbb', NA, NA, 'foofoofoo', NA, NA, NA]
tm.assert_isinstance(rs, Series)
tm.assert_almost_equal(rs, xp)
# unicode
values = Series([u('a'), u('b'), NA, u('c'), NA,
u('d')])
result = values.str.repeat(3)
exp = Series([u('aaa'), u('bbb'), NA, u('ccc'), NA,
u('ddd')])
tm.assert_series_equal(result, exp)
result = values.str.repeat([1, 2, 3, 4, 5, 6])
exp = Series([u('a'), u('bb'), NA, u('cccc'), NA,
u('dddddd')])
tm.assert_series_equal(result, exp)
def test_scalars_properties(self, plasma, reference, config, attr):
actual = getattr(plasma, attr)
if hasattr(actual, 'cgs'):
actual = actual.cgs.value
expected = reference.select(os.path.join(
config.plasma.save_path, 'plasma', 'scalars'))[attr]
pdt.assert_almost_equal(actual, expected)
def test_as_matrix(self):
frame = self.frame
mat = frame.as_matrix()
frameCols = frame.columns
for i, row in enumerate(mat):
for j, value in enumerate(row):
col = frameCols[j]
if np.isnan(value):
assert np.isnan(frame[col][i])
else:
assert value == frame[col][i]
# mixed type
mat = self.mixed_frame.as_matrix(['foo', 'A'])
assert mat[0, 0] == 'bar'
df = DataFrame({'real': [1, 2, 3], 'complex': [1j, 2j, 3j]})
mat = df.as_matrix()
assert mat[0, 0] == 1j
# single block corner case
mat = self.frame.as_matrix(['A', 'B'])
expected = self.frame.reindex(columns=['A', 'B']).values
assert_almost_equal(mat, expected)
def test_arraylike(self):
data = [.2, 1.4, 2.5, 6.2, 9.7, 2.1]
result, bins = cut(data, 3, retbins=True)
exp_codes = np.array([0, 0, 0, 1, 2, 0], dtype=np.int8)
tm.assert_numpy_array_equal(result.codes, exp_codes)
exp = np.array([0.1905, 3.36666667, 6.53333333, 9.7])
tm.assert_almost_equal(bins, exp)
def test_noright(self):
data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
result, bins = cut(data, 4, right=False, retbins=True)
exp_codes = np.array([0, 0, 0, 2, 3, 0, 1], dtype=np.int8)
tm.assert_numpy_array_equal(result.codes, exp_codes)
exp = np.array([0.2, 2.575, 4.95, 7.325, 9.7095])
tm.assert_almost_equal(bins, exp)
def test_col_startswith(self):
db = self.db
ret = db.names.startswith('mi')
assert tm.assert_almost_equal(ret.index, pd.Series([3,4,8,9]))
ret = db.names.startswith('dal')
assert tm.assert_almost_equal(ret.index, pd.Series([0,5]))
def test_endswith(self):
values = Series(['om', NA, 'foo_nom', 'nom', 'bar_foo', NA, 'foo'])
result = values.str.endswith('foo')
exp = Series([False, NA, False, False, True, NA, True])
tm.assert_series_equal(result, exp)
# mixed
mixed = ['a', NA, 'b', True, datetime.today(), 'foo', None, 1, 2.]
rs = strings.str_endswith(mixed, 'f')
xp = [False, NA, False, NA, NA, False, NA, NA, NA]
tm.assert_almost_equal(rs, xp)
rs = Series(mixed).str.endswith('f')
tm.assert_isinstance(rs, Series)
tm.assert_almost_equal(rs, xp)
# unicode
values = Series([u('om'), NA, u('foo_nom'), u('nom'), u('bar_foo'), NA,
u('foo')])
result = values.str.endswith('foo')
exp = Series([False, NA, False, False, True, NA, True])
tm.assert_series_equal(result, exp)
result = values.str.endswith('foo', na=False)
tm.assert_series_equal(result, exp.fillna(False).astype(bool))
def test_y_predict(self):
y = tm.makeTimeSeries()
x = tm.makeTimeDataFrame()
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
model1 = ols(y=y, x=x)
assert_series_equal(model1.y_predict, model1.y_fitted)
assert_almost_equal(model1._y_predict_raw, model1._y_fitted_raw)
def test_lower_upper(self):
values = Series(['om', NA, 'nom', 'nom'])
result = values.str.upper()
exp = Series(['OM', NA, 'NOM', 'NOM'])
tm.assert_series_equal(result, exp)
result = result.str.lower()
tm.assert_series_equal(result, values)
# mixed
mixed = Series(['a', NA, 'b', True, datetime.today(), 'foo', None,
1, 2.])
mixed = mixed.str.upper()
rs = Series(mixed).str.lower()
xp = ['a', NA, 'b', NA, NA, 'foo', NA, NA, NA]
tm.assert_isinstance(rs, Series)
tm.assert_almost_equal(rs, xp)
# unicode
values = Series([u('om'), NA, u('nom'), u('nom')])
result = values.str.upper()
exp = Series([u('OM'), NA, u('NOM'), u('NOM')])
tm.assert_series_equal(result, exp)
result = result.str.lower()
tm.assert_series_equal(result, values)
def test_int_index(self):
from pandas.core.series import Series
arr = np.random.randn(100, 4)
result = reduction.reduce(arr, np.sum, labels=Index(np.arange(4)))
expected = arr.sum(0)
assert_almost_equal(result, expected)
result = reduction.reduce(arr, np.sum, axis=1,
labels=Index(np.arange(100)))
expected = arr.sum(1)
assert_almost_equal(result, expected)
dummy = Series(0., index=np.arange(100))
result = reduction.reduce(arr, np.sum, dummy=dummy,
labels=Index(np.arange(4)))
expected = arr.sum(0)
assert_almost_equal(result, expected)
dummy = Series(0., index=np.arange(4))
result = reduction.reduce(arr, np.sum, axis=1, dummy=dummy,
labels=Index(np.arange(100)))
expected = arr.sum(1)
assert_almost_equal(result, expected)
result = reduction.reduce(arr, np.sum, axis=1, dummy=dummy,
labels=Index(np.arange(100)))
assert_almost_equal(result, expected)
def test_deprecated_match(self):
# Old match behavior, deprecated (but still default) in 0.13
values = Series(['fooBAD__barBAD', NA, 'foo'])
with tm.assert_produces_warning():
result = values.str.match('.*(BAD[_]+).*(BAD)')
exp = Series([('BAD__', 'BAD'), NA, []])
tm.assert_series_equal(result, exp)
# mixed
mixed = Series(['aBAD_BAD', NA, 'BAD_b_BAD', True, datetime.today(),
'foo', None, 1, 2.])
with tm.assert_produces_warning():
rs = Series(mixed).str.match('.*(BAD[_]+).*(BAD)')
xp = [('BAD_', 'BAD'), NA, ('BAD_', 'BAD'), NA, NA, [], NA, NA, NA]
tm.assert_isinstance(rs, Series)
tm.assert_almost_equal(rs, xp)
# unicode
values = Series([u('fooBAD__barBAD'), NA, u('foo')])
with tm.assert_produces_warning():
result = values.str.match('.*(BAD[_]+).*(BAD)')
exp = Series([(u('BAD__'), u('BAD')), NA, []])
tm.assert_series_equal(result, exp)
def _check(dtype):
obj = np.array(np.random.randn(20), dtype=dtype)
bins = np.array([6, 12, 20])
out = np.zeros((3, 4), dtype)
counts = np.zeros(len(out), dtype=np.int64)
labels = _ensure_int64(np.repeat(np.arange(3),
np.diff(np.r_[0, bins])))
func = getattr(groupby, 'group_ohlc_%s' % dtype)
func(out, counts, obj[:, None], labels)
def _ohlc(group):
if isna(group).all():
return np.repeat(nan, 4)
return [group[0], group.max(), group.min(), group[-1]]
expected = np.array([_ohlc(obj[:6]), _ohlc(obj[6:12]),
_ohlc(obj[12:])])
assert_almost_equal(out, expected)
tm.assert_numpy_array_equal(counts,
np.array([6, 6, 8], dtype=np.int64))
obj[:6] = nan
func(out, counts, obj[:, None], labels)
expected[0] = nan
assert_almost_equal(out, expected)
def test_split(self):
values = Series(['a_b_c', 'c_d_e', NA, 'f_g_h'])
result = values.str.split('_')
exp = Series([['a', 'b', 'c'], ['c', 'd', 'e'], NA, ['f', 'g', 'h']])
tm.assert_series_equal(result, exp)
# more than one char
values = Series(['a__b__c', 'c__d__e', NA, 'f__g__h'])
result = values.str.split('__')
tm.assert_series_equal(result, exp)
# mixed
mixed = Series(['a_b_c', NA, 'd_e_f', True, datetime.today(),
None, 1, 2.])
rs = Series(mixed).str.split('_')
xp = Series([['a', 'b', 'c'], NA, ['d', 'e', 'f'], NA, NA,
NA, NA, NA])
tm.assert_isinstance(rs, Series)
tm.assert_almost_equal(rs, xp)
# unicode
values = Series([u('a_b_c'), u('c_d_e'), NA, u('f_g_h')])
result = values.str.split('_')
exp = Series([[u('a'), u('b'), u('c')],
[u('c'), u('d'), u('e')], NA,
[u('f'), u('g'), u('h')]])
tm.assert_series_equal(result, exp)
def test_to_records_with_unicode_index(self):
# GH13172
# unicode_literals conflict with to_records
result = DataFrame([{'a': 'x', 'b': 'y'}]).set_index('a') \
.to_records()
expected = np.rec.array([('x', 'y')], dtype=[('a', 'O'), ('b', 'O')])
tm.assert_almost_equal(result, expected)
def test_get(self):
values = Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'])
result = values.str.split('_').str.get(1)
expected = Series(['b', 'd', np.nan, 'g'])
tm.assert_series_equal(result, expected)
# mixed
mixed = Series(['a_b_c', NA, 'c_d_e', True, datetime.today(),
None, 1, 2.])
rs = Series(mixed).str.split('_').str.get(1)
xp = Series(['b', NA, 'd', NA, NA,
NA, NA, NA])
tm.assert_isinstance(rs, Series)
tm.assert_almost_equal(rs, xp)
# unicode
values = Series([u('a_b_c'), u('c_d_e'), np.nan,
u('f_g_h')])
result = values.str.split('_').str.get(1)
expected = Series([u('b'), u('d'), np.nan, u('g')])
tm.assert_series_equal(result, expected)
def test_to_timedelta(self):
def conv(v):
return v.astype('m8[ns]')
d1 = np.timedelta64(1, 'D')
self.assertEqual(
to_timedelta('1 days 06:05:01.00003', box=False),
conv(d1 + np.timedelta64(6 * 3600 + 5 * 60 + 1, 's') +
np.timedelta64(30, 'us')))
self.assertEqual(to_timedelta('15.5us', box=False),
conv(np.timedelta64(15500, 'ns')))
# empty string
result = to_timedelta('', box=False)
self.assertEqual(result.astype('int64'), tslib.iNaT)
result = to_timedelta(['', ''])
self.assertTrue(isnull(result).all())
# pass thru
result = to_timedelta(np.array([np.timedelta64(1, 's')]))
expected = np.array([np.timedelta64(1, 's')])
tm.assert_almost_equal(result, expected)
# ints
result = np.timedelta64(0, 'ns')
expected = to_timedelta(0, box=False)
self.assertEqual(result, expected)
# Series
expected = Series([timedelta(days=1), timedelta(days=1, seconds=1)])
result = to_timedelta(Series(['1d', '1days 00:00:01']))
tm.assert_series_equal(result, expected)
# with units
result = TimedeltaIndex([
np.timedelta64(0, 'ns'),
np.timedelta64(10, 's').astype('m8[ns]')
])
expected = to_timedelta([0, 10], unit='s')
tm.assert_index_equal(result, expected)
# single element conversion
v = timedelta(seconds=1)
result = to_timedelta(v, box=False)
expected = np.timedelta64(timedelta(seconds=1))
self.assertEqual(result, expected)
v = np.timedelta64(timedelta(seconds=1))
result = to_timedelta(v, box=False)
expected = np.timedelta64(timedelta(seconds=1))
self.assertEqual(result, expected)
# arrays of various dtypes
arr = np.array([1] * 5, dtype='int64')
result = to_timedelta(arr, unit='s')
expected = TimedeltaIndex([np.timedelta64(1, 's')] * 5)
tm.assert_index_equal(result, expected)
arr = np.array([1] * 5, dtype='int64')
result = to_timedelta(arr, unit='m')
expected = TimedeltaIndex([np.timedelta64(1, 'm')] * 5)
tm.assert_index_equal(result, expected)
arr = np.array([1] * 5, dtype='int64')
result = to_timedelta(arr, unit='h')
expected = TimedeltaIndex([np.timedelta64(1, 'h')] * 5)
tm.assert_index_equal(result, expected)
arr = np.array([1] * 5, dtype='timedelta64[s]')
result = to_timedelta(arr)
expected = TimedeltaIndex([np.timedelta64(1, 's')] * 5)
tm.assert_index_equal(result, expected)
arr = np.array([1] * 5, dtype='timedelta64[D]')
result = to_timedelta(arr)
expected = TimedeltaIndex([np.timedelta64(1, 'D')] * 5)
tm.assert_index_equal(result, expected)
def testit(unit, transform):
# array
result = to_timedelta(np.arange(5), unit=unit)
expected = TimedeltaIndex([
np.timedelta64(i, transform(unit))
for i in np.arange(5).tolist()
])
tm.assert_index_equal(result, expected)
# scalar
result = to_timedelta(2, unit=unit)
expected = Timedelta(
np.timedelta64(2, transform(unit)).astype('timedelta64[ns]'))
self.assertEqual(result, expected)
# validate all units
# GH 6855
for unit in ['Y', 'M', 'W', 'D', 'y', 'w', 'd']:
testit(unit, lambda x: x.upper())
for unit in ['days', 'day', 'Day', 'Days']:
testit(unit, lambda x: 'D')
for unit in [
'h', 'm', 's', 'ms', 'us', 'ns', 'H', 'S', 'MS', 'US', 'NS'
]:
testit(unit, lambda x: x.lower())
# offsets
# m
testit('T', lambda x: 'm')
# ms
testit('L', lambda x: 'ms')
# these will error
self.assertRaises(ValueError, lambda: to_timedelta([1, 2], unit='foo'))
self.assertRaises(ValueError, lambda: to_timedelta(1, unit='foo'))
def test_list_float(self):
x = [np.random.rand() for i in range(5)]
x_rec = self.encode_decode(x)
tm.assert_almost_equal(x,x_rec)
def test_constructor_from_sparse(self):
res = SparseArray(self.zarr)
self.assertEqual(res.fill_value, 0)
assert_almost_equal(res.sp_values, self.zarr.sp_values)
def check_line(xpl, rsl):
xpdata = xpl.get_xydata()
rsdata = rsl.get_xydata()
tm.assert_almost_equal(xpdata, rsdata)
def test_list_mixed(self):
x = [1.0, np.float32(3.5), np.complex128(4.25), u('foo')]
x_rec = self.encode_decode(x)
tm.assert_almost_equal(x,x_rec)
def test_left_join_indexer():
a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
b = np.array([0, 3, 5, 7, 9], dtype=np.int64)
index, ares, bres = algos.left_join_indexer_int64(a, b)
assert_almost_equal(index, a)
aexp = np.array([0, 1, 2, 3, 4], dtype=np.int64)
bexp = np.array([-1, -1, 1, -1, 2], dtype=np.int64)
assert_almost_equal(ares, aexp)
assert_almost_equal(bres, bexp)
a = np.array([5], dtype=np.int64)
b = np.array([5], dtype=np.int64)
index, ares, bres = algos.left_join_indexer_int64(a, b)
assert_almost_equal(index, [5])
assert_almost_equal(ares, [0])
assert_almost_equal(bres, [0])
def _check(s, expected, method='average'):
result = s.rank(method=method)
assert_almost_equal(result, expected)
def test_inner_join_indexer():
a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
b = np.array([0, 3, 5, 7, 9], dtype=np.int64)
index, ares, bres = algos.inner_join_indexer_int64(a, b)
index_exp = np.array([3, 5], dtype=np.int64)
assert_almost_equal(index, index_exp)
aexp = np.array([2, 4])
bexp = np.array([1, 2])
assert_almost_equal(ares, aexp)
assert_almost_equal(bres, bexp)
a = np.array([5], dtype=np.int64)
b = np.array([5], dtype=np.int64)
index, ares, bres = algos.inner_join_indexer_int64(a, b)
assert_almost_equal(index, [5])
assert_almost_equal(ares, [0])
assert_almost_equal(bres, [0])
def test_get_value(self):
for idx in self.frame.index:
for col in self.frame.columns:
result = self.frame.get_value(idx, col)
expected = self.frame[col][idx]
tm.assert_almost_equal(result, expected)
