def setUp(self):
self.seriesd = common.getSeriesData()
self.tsd = common.getTimeSeriesData()
self.frame = self.klass(self.seriesd)
self.intframe = self.klass(dict((k, v.astype(int))
for k, v in self.seriesd.iteritems()))
self.tsframe = self.klass(self.tsd)
self.mixed_frame = self.frame.copy()
self.mixed_frame['foo'] = 'bar'
self.ts1 = common.makeTimeSeries()
self.ts2 = common.makeTimeSeries()[5:]
self.ts3 = common.makeTimeSeries()[-5:]
self.ts4 = common.makeTimeSeries()[1:-1]
self.ts_dict = {
'col1' : self.ts1,
'col2' : self.ts2,
'col3' : self.ts3,
'col4' : self.ts4,
}
self.empty = self.klass({})
self.unsortable = self.klass(
{'foo' : [1] * 1000,
datetime.today() : [1] * 1000,
'bar' : ['bar'] * 1000,
datetime.today() + timedelta(1) : ['bar'] * 1000},
index=np.arange(1000))
def test_loc_with_text_dates():
A = tm.makeTimeSeries(10).iloc[:5]
B = tm.makeTimeSeries(10).iloc[5:]
s = dd.Series({('df', 0): A, ('df', 1): B}, 'df', None, [A.index.max()])
assert eq(s.loc['2000': '2010'], s)
assert len(s.loc['2000-01-03': '2000-01-05'].compute()) == 3
def test_loc_with_text_dates():
A = tm.makeTimeSeries(10).iloc[:5]
B = tm.makeTimeSeries(10).iloc[5:]
s = dd.Series({("df", 0): A, ("df", 1): B}, "df", None, [A.index.min(), A.index.max(), B.index.max()])
assert s.loc["2000":"2010"].divisions == s.divisions
assert eq(s.loc["2000":"2010"], s)
assert len(s.loc["2000-01-03":"2000-01-05"].compute()) == 3
def test_series_rhs(self):
y = tm.makeTimeSeries()
x = tm.makeTimeSeries()
model = ols(y=y, x=x)
expected = ols(y=y, x={"x": x})
assert_series_equal(model.beta, expected.beta)
# GH 5233/5250
assert_series_equal(model.y_predict, model.predict(x=x))
def test_concat_bug_1719(self):
ts1 = tm.makeTimeSeries()
ts2 = tm.makeTimeSeries()[::2]
# to join with union
# these two are of different length!
left = concat([ts1, ts2], join='outer', axis=1)
right = concat([ts2, ts1], join='outer', axis=1)
self.assertEqual(len(left), len(right))
def test_series_rhs(self):
y = tm.makeTimeSeries()
x = tm.makeTimeSeries()
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
model = ols(y=y, x=x)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = ols(y=y, x={'x': x})
assert_series_equal(model.beta, expected.beta)
# GH 5233/5250
assert_series_equal(model.y_predict, model.predict(x=x))
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
result = moments.rolling_corr(A, B, 50, min_periods=25)
assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
# test for correct bias correction
a = tm.makeTimeSeries()
b = tm.makeTimeSeries()
a[:5] = np.nan
b[:10] = np.nan
result = moments.rolling_corr(a, b, len(a), min_periods=1)
assert_almost_equal(result[-1], a.corr(b))
def test_plot_offset_freq(self):
ser = tm.makeTimeSeries()
_check_plot_works(ser.plot)
dr = date_range(ser.index[0], freq='BQS', periods=10)
ser = Series(np.random.randn(len(dr)), dr)
_check_plot_works(ser.plot)
def test_series_agg_multikey():
ts = tm.makeTimeSeries()
grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.agg(np.sum)
expected = grouped.sum()
tm.assert_series_equal(result, expected)
def test_concat_series_axis1(self):
ts = tm.makeTimeSeries()
pieces = [ts[:-2], ts[2:], ts[2:-2]]
result = concat(pieces, axis=1)
expected = DataFrame(pieces).T
assert_frame_equal(result, expected)
result = concat(pieces, keys=['A', 'B', 'C'], axis=1)
expected = DataFrame(pieces, index=['A', 'B', 'C']).T
assert_frame_equal(result, expected)
# preserve series names, #2489
s = Series(randn(5), name='A')
s2 = Series(randn(5), name='B')
result = concat([s, s2], axis=1)
expected = DataFrame({'A': s, 'B': s2})
assert_frame_equal(result, expected)
s2.name = None
result = concat([s, s2], axis=1)
self.assertTrue(np.array_equal(
result.columns, Index(['A', 0], dtype='object')))
# must reindex, #2603
s = Series(randn(3), index=['c', 'a', 'b'], name='A')
s2 = Series(randn(4), index=['d', 'a', 'b', 'c'], name='B')
result = concat([s, s2], axis=1)
expected = DataFrame({'A': s, 'B': s2})
assert_frame_equal(result, expected)
def test_series_describe_multikey(self):
ts = tm.makeTimeSeries()
grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.describe()
assert_series_equal(result['mean'], grouped.mean(), check_names=False)
assert_series_equal(result['std'], grouped.std(), check_names=False)
assert_series_equal(result['min'], grouped.min(), check_names=False)
def test_isnull():
assert not isnull(1.)
assert isnull(None)
assert isnull(np.NaN)
assert not isnull(np.inf)
assert not isnull(-np.inf)
# series
for s in [tm.makeFloatSeries(),tm.makeStringSeries(),
tm.makeObjectSeries(),tm.makeTimeSeries(),tm.makePeriodSeries()]:
assert(isinstance(isnull(s), Series))
# frame
for df in [tm.makeTimeDataFrame(),tm.makePeriodFrame(),tm.makeMixedDataFrame()]:
result = isnull(df)
expected = df.apply(isnull)
tm.assert_frame_equal(result, expected)
# panel
for p in [ tm.makePanel(), tm.makePeriodPanel(), tm.add_nans(tm.makePanel()) ]:
result = isnull(p)
expected = p.apply(isnull)
tm.assert_panel_equal(result, expected)
# panel 4d
for p in [ tm.makePanel4D(), tm.add_nans_panel4d(tm.makePanel4D()) ]:
result = isnull(p)
expected = p.apply(isnull)
tm.assert_panel4d_equal(result, expected)
def setUp(self):
TestPlotBase.setUp(self)
import matplotlib as mpl
mpl.rcdefaults()
self.ts = tm.makeTimeSeries()
self.ts.name = 'ts'
def test_catch_regressor_overlap(self):
df1 = tm.makeTimeDataFrame().ix[:, ["A", "B"]]
df2 = tm.makeTimeDataFrame().ix[:, ["B", "C", "D"]]
y = tm.makeTimeSeries()
data = {"foo": df1, "bar": df2}
self.assertRaises(Exception, ols, y=y, x=data)
def test_both_style_and_color(self):
ts = tm.makeTimeSeries()
pytest.raises(ValueError, ts.plot, style='b-', color='#000099')
s = ts.reset_index(drop=True)
pytest.raises(ValueError, s.plot, style='b-', color='#000099')
def test_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.predict(), model1.y_predict)
assert_series_equal(model1.predict(x=x), model1.y_predict)
assert_series_equal(model1.predict(beta=model1.beta), model1.y_predict)
exog = x.copy()
exog['intercept'] = 1.
rs = Series(np.dot(exog.values, model1.beta.values), x.index)
assert_series_equal(model1.y_predict, rs)
x2 = x.reindex(columns=x.columns[::-1])
assert_series_equal(model1.predict(x=x2), model1.y_predict)
x3 = x2 + 10
pred3 = model1.predict(x=x3)
x3['intercept'] = 1.
x3 = x3.reindex(columns=model1.beta.index)
expected = Series(np.dot(x3.values, model1.beta.values), x3.index)
assert_series_equal(expected, pred3)
beta = Series(0., model1.beta.index)
pred4 = model1.predict(beta=beta)
assert_series_equal(Series(0., pred4.index), pred4)
def test_predict(self):
y = tm.makeTimeSeries()
x = tm.makeTimeDataFrame()
model1 = ols(y=y, x=x)
assert_series_equal(model1.predict(), model1.y_predict)
assert_series_equal(model1.predict(x=x), model1.y_predict)
assert_series_equal(model1.predict(beta=model1.beta), model1.y_predict)
exog = x.copy()
exog["intercept"] = 1.0
rs = Series(np.dot(exog.values, model1.beta.values), x.index)
assert_series_equal(model1.y_predict, rs)
x2 = x.reindex(columns=x.columns[::-1])
assert_series_equal(model1.predict(x=x2), model1.y_predict)
x3 = x2 + 10
pred3 = model1.predict(x=x3)
x3["intercept"] = 1.0
x3 = x3.reindex(columns=model1.beta.index)
expected = Series(np.dot(x3.values, model1.beta.values), x3.index)
assert_series_equal(expected, pred3)
beta = Series(0.0, model1.beta.index)
pred4 = model1.predict(beta=beta)
assert_series_equal(Series(0.0, pred4.index), pred4)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
# pathing
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('foo')
self.store.remove('b/foo')
self.assertEquals(len(self.store), 1)
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('b')
self.assertEquals(len(self.store), 1)
# __delitem__
self.store['a'] = ts
self.store['b'] = df
del self.store['a']
del self.store['b']
self.assertEquals(len(self.store), 0)
def test_mixed_freq_irreg_period(self):
ts = tm.makeTimeSeries()
irreg = ts[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 16, 17, 18, 29]]
rng = period_range('1/3/2000', periods=30, freq='B')
ps = Series(np.random.randn(len(rng)), rng)
irreg.plot()
ps.plot()
def test_notnull():
assert notnull(1.)
assert not notnull(None)
assert not notnull(np.NaN)
with cf.option_context("mode.use_inf_as_null", False):
assert notnull(np.inf)
assert notnull(-np.inf)
arr = np.array([1.5, np.inf, 3.5, -np.inf])
result = notnull(arr)
assert result.all()
with cf.option_context("mode.use_inf_as_null", True):
assert not notnull(np.inf)
assert not notnull(-np.inf)
arr = np.array([1.5, np.inf, 3.5, -np.inf])
result = notnull(arr)
assert result.sum() == 2
with cf.option_context("mode.use_inf_as_null", False):
for s in [tm.makeFloatSeries(),tm.makeStringSeries(),
tm.makeObjectSeries(),tm.makeTimeSeries(),tm.makePeriodSeries()]:
assert(isinstance(isnull(s), Series))
def test_len_keys(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.assertEquals(len(self.store), 4)
self.assert_(set(self.store.keys()) == set(['a', 'b', 'c', 'd']))
def setup_method(self, method):
TestPlotBase.setup_method(self, method)
import matplotlib as mpl
mpl.rcdefaults()
self.ts = tm.makeTimeSeries()
self.ts.name = 'ts'
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
self.assertRaises(AttributeError, self.store.get, 'b')
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
repr(self.store)
def setUp(self):
self.ts = tm.makeTimeSeries()
self.seriesd = tm.getSeriesData()
self.tsd = tm.getTimeSeriesData()
self.frame = DataFrame(self.seriesd)
self.tsframe = DataFrame(self.tsd)
self.df = df()
self.df_mixed_floats = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.array(
np.random.randn(8), dtype='float32')})
self.mframe = mframe()
self.three_group = DataFrame(
{'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar',
'foo', 'foo', 'foo'],
'B': ['one', 'one', 'one', 'two', 'one', 'one', 'one', 'two',
'two', 'two', 'one'],
'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny',
'dull', 'shiny', 'shiny', 'shiny'],
'D': np.random.randn(11),
'E': np.random.randn(11),
'F': np.random.randn(11)})
def test_catch_regressor_overlap(self):
df1 = tm.makeTimeDataFrame().ix[:, ['A', 'B']]
df2 = tm.makeTimeDataFrame().ix[:, ['B', 'C', 'D']]
y = tm.makeTimeSeries()
data = {'foo' : df1, 'bar' : df2}
self.assertRaises(Exception, ols, y=y, x=data)
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 datetime_series():
"""
Fixture for Series of floats with DatetimeIndex
"""
s = tm.makeTimeSeries()
s.name = 'ts'
return s
def test_flex_method_equivalence(self, opname, ts):
# check that Series.{opname} behaves like Series.__{opname}__,
tser = tm.makeTimeSeries().rename('ts')
series = ts[0](tser)
other = ts[1](tser)
check_reverse = ts[2]
if opname == 'div' and compat.PY3:
pytest.skip('div test only for Py3')
op = getattr(Series, opname)
if op == 'div':
alt = operator.truediv
else:
alt = getattr(operator, opname)
result = op(series, other)
expected = alt(series, other)
tm.assert_almost_equal(result, expected)
if check_reverse:
rop = getattr(Series, "r" + opname)
result = rop(series, other)
expected = alt(other, series)
tm.assert_almost_equal(result, expected)
def test_divmod(self):
def check(series, other):
results = divmod(series, other)
if isinstance(other, Iterable) and len(series) != len(other):
# if the lengths don't match, this is the test where we use
# `tser[::2]`. Pad every other value in `other_np` with nan.
other_np = []
for n in other:
other_np.append(n)
other_np.append(np.nan)
else:
other_np = other
other_np = np.asarray(other_np)
with np.errstate(all='ignore'):
expecteds = divmod(series.values, np.asarray(other_np))
for result, expected in zip(results, expecteds):
# check the values, name, and index separately
tm.assert_almost_equal(np.asarray(result), expected)
assert result.name == series.name
tm.assert_index_equal(result.index, series.index)
tser = tm.makeTimeSeries().rename('ts')
check(tser, tser * 2)
check(tser, tser * 0)
check(tser, tser[::2])
check(tser, 5)
def test_series_operators(self):
def _check_op(series, other, op, pos_only=False, check_dtype=True):
left = np.abs(series) if pos_only else series
right = np.abs(other) if pos_only else other
cython_or_numpy = op(left, right)
python = left.combine(right, op)
tm.assert_series_equal(cython_or_numpy, python, check_dtype=check_dtype)
def check(series, other):
simple_ops = ["add", "sub", "mul", "truediv", "floordiv", "mod"]
for opname in simple_ops:
_check_op(series, other, getattr(operator, opname))
_check_op(series, other, operator.pow, pos_only=True)
_check_op(series, other, lambda x, y: operator.add(y, x))
_check_op(series, other, lambda x, y: operator.sub(y, x))
_check_op(series, other, lambda x, y: operator.truediv(y, x))
_check_op(series, other, lambda x, y: operator.floordiv(y, x))
_check_op(series, other, lambda x, y: operator.mul(y, x))
_check_op(series, other, lambda x, y: operator.pow(y, x), pos_only=True)
_check_op(series, other, lambda x, y: operator.mod(y, x))
tser = tm.makeTimeSeries().rename("ts")
check(tser, tser * 2)
check(tser, tser * 0)
check(tser, tser[::2])
check(tser, 5)
def check_comparators(series, other, check_dtype=True):
_check_op(series, other, operator.gt, check_dtype=check_dtype)
_check_op(series, other, operator.ge, check_dtype=check_dtype)
_check_op(series, other, operator.eq, check_dtype=check_dtype)
_check_op(series, other, operator.lt, check_dtype=check_dtype)
_check_op(series, other, operator.le, check_dtype=check_dtype)
check_comparators(tser, 5)
check_comparators(tser, tser + 1, check_dtype=False)
def setup_method(self, method):
self.ts = tm.makeTimeSeries()
self.seriesd = tm.getSeriesData()
self.tsd = tm.getTimeSeriesData()
self.frame = DataFrame(self.seriesd)
self.tsframe = DataFrame(self.tsd)
self.df = df()
self.df_mixed_floats = DataFrame({
'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C':
np.random.randn(8),
'D':
np.array(np.random.randn(8), dtype='float32')
})
self.mframe = mframe()
self.three_group = DataFrame({
'A': [
'foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar', 'foo',
'foo', 'foo'
],
'B': [
'one', 'one', 'one', 'two', 'one', 'one', 'one', 'two', 'two',
'two', 'one'
],
'C': [
'dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny',
'dull', 'shiny', 'shiny', 'shiny'
],
'D':
np.random.randn(11),
'E':
np.random.randn(11),
'F':
np.random.randn(11)
})
def test_isna_isnull(self, isna_f):
assert not isna_f(1.)
assert isna_f(None)
assert isna_f(np.NaN)
assert float('nan')
assert not isna_f(np.inf)
assert not isna_f(-np.inf)
# series
for s in [
tm.makeFloatSeries(),
tm.makeStringSeries(),
tm.makeObjectSeries(),
tm.makeTimeSeries(),
tm.makePeriodSeries()
]:
assert isinstance(isna_f(s), Series)
# frame
for df in [
tm.makeTimeDataFrame(),
tm.makePeriodFrame(),
tm.makeMixedDataFrame()
]:
result = isna_f(df)
expected = df.apply(isna_f)
tm.assert_frame_equal(result, expected)
# panel
with catch_warnings(record=True):
simplefilter("ignore", FutureWarning)
for p in [
tm.makePanel(),
tm.makePeriodPanel(),
tm.add_nans(tm.makePanel())
]:
result = isna_f(p)
expected = p.apply(isna_f)
tm.assert_panel_equal(result, expected)
def test_isnull():
assert not isnull(1.)
assert isnull(None)
assert isnull(np.NaN)
assert not isnull(np.inf)
assert not isnull(-np.inf)
for s in [
tm.makeFloatSeries(),
tm.makeStringSeries(),
tm.makeObjectSeries(),
tm.makeTimeSeries(),
tm.makePeriodSeries()
]:
assert (isinstance(isnull(s), Series))
# call on DataFrame
df = DataFrame(np.random.randn(10, 5))
df['foo'] = 'bar'
result = isnull(df)
expected = result.apply(isnull)
tm.assert_frame_equal(result, expected)
def test_concat_series(self):
ts = tm.makeTimeSeries()
ts.name = 'foo'
pieces = [ts[:5], ts[5:15], ts[15:]]
result = concat(pieces)
tm.assert_series_equal(result, ts)
self.assertEqual(result.name, ts.name)
result = concat(pieces, keys=[0, 1, 2])
expected = ts.copy()
ts.index = DatetimeIndex(np.array(ts.index.values, dtype='M8[ns]'))
exp_labels = [np.repeat([0, 1, 2], [len(x) for x in pieces]),
np.arange(len(ts))]
exp_index = MultiIndex(levels=[[0, 1, 2], ts.index],
labels=exp_labels)
expected.index = exp_index
tm.assert_series_equal(result, expected)
def setUp(self):
self.ts = tm.makeTimeSeries()
self.seriesd = tm.getSeriesData()
self.tsd = tm.getTimeSeriesData()
self.frame = DataFrame(self.seriesd)
self.tsframe = DataFrame(self.tsd)
self.df = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
self.df_mixed_floats = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.array(
np.random.randn(8), dtype='float32')})
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['first', 'second'])
self.mframe = DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
self.three_group = DataFrame(
{'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar',
'foo', 'foo', 'foo'],
'B': ['one', 'one', 'one', 'two', 'one', 'one', 'one', 'two',
'two', 'two', 'one'],
'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny',
'dull', 'shiny', 'shiny', 'shiny'],
'D': np.random.randn(11),
'E': np.random.randn(11),
'F': np.random.randn(11)})
def test_concat_series(self):
ts = tm.makeTimeSeries()
ts.name = 'foo'
pieces = [ts[:5], ts[5:15], ts[15:]]
result = concat(pieces)
tm.assert_series_equal(result, ts)
self.assertEqual(result.name, ts.name)
result = concat(pieces, keys=[0, 1, 2])
expected = ts.copy()
exp_labels = [
np.repeat([0, 1, 2], [len(x) for x in pieces]),
np.arange(len(ts))
]
exp_index = MultiIndex(levels=[[0, 1, 2], ts.index], labels=exp_labels)
expected.index = exp_index
tm.assert_series_equal(result, expected)
self.assertRaises(Exception, concat, pieces, axis=1)
def test_series_frame_radd_bug(self):
# GH#353
vals = pd.Series(tm.rands_array(5, 10))
result = 'foo_' + vals
expected = vals.map(lambda x: 'foo_' + x)
tm.assert_series_equal(result, expected)
frame = pd.DataFrame({'vals': vals})
result = 'foo_' + frame
expected = pd.DataFrame({'vals': vals.map(lambda x: 'foo_' + x)})
tm.assert_frame_equal(result, expected)
ts = tm.makeTimeSeries()
ts.name = 'ts'
# really raise this time
now = pd.Timestamp.now().to_pydatetime()
with pytest.raises(TypeError):
now + ts
with pytest.raises(TypeError):
ts + now
def test_tsplot(self):
from pandas.tseries.plotting import tsplot
import matplotlib.pyplot as plt
plt.close('all')
ax = plt.gca()
ts = tm.makeTimeSeries()
tsplot(ts, plt.Axes.plot)
f = lambda *args, **kwds: tsplot(s, plt.Axes.plot, *args, **kwds)
plt.close('all')
for s in self.period_ser:
_check_plot_works(f, s.index.freq, ax=ax, series=s)
plt.close('all')
for s in self.datetime_ser:
_check_plot_works(f, s.index.freq.rule_code, ax=ax, series=s)
plt.close('all')
plt.close('all')
ax = ts.plot(style='k')
self.assert_((0., 0., 0.) == ax.get_lines()[0].get_color())
def test_axis_limits(self):
def _test(ax):
xlim = ax.get_xlim()
ax.set_xlim(xlim[0] - 5, xlim[1] + 10)
ax.get_figure().canvas.draw()
result = ax.get_xlim()
self.assertEqual(result[0], xlim[0] - 5)
self.assertEqual(result[1], xlim[1] + 10)
# string
expected = (Period('1/1/2000',
ax.freq), Period('4/1/2000', ax.freq))
ax.set_xlim('1/1/2000', '4/1/2000')
ax.get_figure().canvas.draw()
result = ax.get_xlim()
self.assertEqual(int(result[0]), expected[0].ordinal)
self.assertEqual(int(result[1]), expected[1].ordinal)
# datetim
expected = (Period('1/1/2000',
ax.freq), Period('4/1/2000', ax.freq))
ax.set_xlim(datetime(2000, 1, 1), datetime(2000, 4, 1))
ax.get_figure().canvas.draw()
result = ax.get_xlim()
self.assertEqual(int(result[0]), expected[0].ordinal)
self.assertEqual(int(result[1]), expected[1].ordinal)
ser = tm.makeTimeSeries()
ax = ser.plot()
_test(ax)
df = DataFrame({'a': ser, 'b': ser + 1})
ax = df.plot()
_test(ax)
df = DataFrame({'a': ser, 'b': ser + 1})
axes = df.plot(subplots=True)
[_test(ax) for ax in axes]
def test_hash_pandas_object(self):
for obj in [
Series([1, 2, 3]),
Series([1.0, 1.5, 3.2]),
Series([1.0, 1.5, np.nan]),
Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]),
Series(['a', 'b', 'c']),
Series(['a', np.nan, 'c']),
Series(['a', None, 'c']),
Series([True, False, True]),
Series(),
Index([1, 2, 3]),
Index([True, False, True]),
DataFrame({
'x': ['a', 'b', 'c'],
'y': [1, 2, 3]
}),
DataFrame(),
tm.makeMissingDataframe(),
tm.makeMixedDataFrame(),
tm.makeTimeDataFrame(),
tm.makeTimeSeries(),
tm.makeTimedeltaIndex(),
tm.makePeriodIndex(),
Series(tm.makePeriodIndex()),
Series(pd.date_range('20130101', periods=3, tz='US/Eastern')),
MultiIndex.from_product([
range(5), ['foo', 'bar', 'baz'],
pd.date_range('20130101', periods=2)
]),
MultiIndex.from_product(
[pd.CategoricalIndex(list('aabc')),
range(3)])
]:
self.check_equal(obj)
self.check_not_equal_with_index(obj)
def test_r2_no_intercept(self):
y = tm.makeTimeSeries()
x = tm.makeTimeDataFrame()
x_with = x.copy()
x_with['intercept'] = 1.
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
model1 = ols(y=y, x=x)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
model2 = ols(y=y, x=x_with, intercept=False)
assert_series_equal(model1.beta, model2.beta)
# TODO: can we infer whether the intercept is there...
self.assertNotEqual(model1.r2, model2.r2)
# rolling
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
model1 = ols(y=y, x=x, window=20)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
model2 = ols(y=y, x=x_with, window=20, intercept=False)
assert_frame_equal(model1.beta, model2.beta)
self.assertTrue((model1.r2 != model2.r2).all())
def test_tsplot(self):
from pandas.tseries.plotting import tsplot
import matplotlib.pyplot as plt
ax = plt.gca()
ts = tm.makeTimeSeries()
f = lambda *args, **kwds: tsplot(s, plt.Axes.plot, *args, **kwds)
for s in self.period_ser:
_check_plot_works(f, s.index.freq, ax=ax, series=s)
for s in self.datetime_ser:
_check_plot_works(f, s.index.freq.rule_code, ax=ax, series=s)
for s in self.period_ser:
_check_plot_works(s.plot, ax=ax)
for s in self.datetime_ser:
_check_plot_works(s.plot, ax=ax)
ax = ts.plot(style='k')
color = (0., 0., 0., 1) if self.mpl_ge_2_0_0 else (0., 0., 0.)
self.assertEqual(color, ax.get_lines()[0].get_color())
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def setUp(self):
self.ts = tm.makeTimeSeries()
def setup_method(self):
self.ts = tm.makeTimeSeries() # Was at top level in test_series
self.ts.name = "ts"
self.series = tm.makeStringSeries()
self.series.name = "series"
def test_y_predict(self):
y = tm.makeTimeSeries()
x = tm.makeTimeDataFrame()
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 datetime_series_short():
"""
Fixture for Series of floats with DatetimeIndex
"""
return tm.makeTimeSeries(nper=30)[5:]
def setUp(self):
self.ts = tm.makeTimeSeries() # Was at top level in test_series
self.ts.name = 'ts'
self.series = tm.makeStringSeries()
self.series.name = 'series'
from pandas.util._decorators import cache_readonly
import pandas.util.testing as tm
import pandas as pd
_ts = tm.makeTimeSeries()
class TestData(object):
@cache_readonly
def ts(self):
ts = _ts.copy()
ts.name = 'ts'
return ts
@cache_readonly
def series(self):
series = tm.makeStringSeries()
series.name = 'series'
return series
@cache_readonly
def objSeries(self):
objSeries = tm.makeObjectSeries()
objSeries.name = 'objects'
return objSeries
@cache_readonly
def empty(self):
return pd.Series([], index=[])
def ts():
return tm.makeTimeSeries()
def test_series_rhs(self):
y = tm.makeTimeSeries()
x = tm.makeTimeSeries()
model = ols(y=y, x=x)
expected = ols(y=y, x={'x' : x})
assert_series_equal(model.beta, expected.beta)
def test_dataframe(self):
bts = DataFrame({'a': tm.makeTimeSeries()})
ax = bts.plot()
idx = ax.get_lines()[0].get_xdata()
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_dataframe(self):
bts = DataFrame({'a': tm.makeTimeSeries()})
ax = bts.plot()
idx = ax.get_lines()[0].get_xdata()
tm.assert_index_equal(bts.index.to_period(), PeriodIndex(idx))
def ts2(self):
return tm.makeTimeSeries(nper=30)[5:]
def test_series_describe_single():
ts = tm.makeTimeSeries()
grouped = ts.groupby(lambda x: x.month)
result = grouped.apply(lambda x: x.describe())
expected = grouped.describe().stack()
tm.assert_series_equal(result, expected)
def test_reindex(self, float_frame):
datetime_series = tm.makeTimeSeries(nper=30)
newFrame = float_frame.reindex(datetime_series.index)
for col in newFrame.columns:
for idx, val in newFrame[col].items():
if idx in float_frame.index:
if np.isnan(val):
assert np.isnan(float_frame[col][idx])
else:
assert val == float_frame[col][idx]
else:
assert np.isnan(val)
for col, series in newFrame.items():
assert tm.equalContents(series.index, newFrame.index)
emptyFrame = float_frame.reindex(Index([]))
assert len(emptyFrame.index) == 0
# Cython code should be unit-tested directly
nonContigFrame = float_frame.reindex(datetime_series.index[::2])
for col in nonContigFrame.columns:
for idx, val in nonContigFrame[col].items():
if idx in float_frame.index:
if np.isnan(val):
assert np.isnan(float_frame[col][idx])
else:
assert val == float_frame[col][idx]
else:
assert np.isnan(val)
for col, series in nonContigFrame.items():
assert tm.equalContents(series.index, nonContigFrame.index)
# corner cases
# Same index, copies values but not index if copy=False
newFrame = float_frame.reindex(float_frame.index, copy=False)
assert newFrame.index is float_frame.index
# length zero
newFrame = float_frame.reindex([])
assert newFrame.empty
assert len(newFrame.columns) == len(float_frame.columns)
# length zero with columns reindexed with non-empty index
newFrame = float_frame.reindex([])
newFrame = newFrame.reindex(float_frame.index)
assert len(newFrame.index) == len(float_frame.index)
assert len(newFrame.columns) == len(float_frame.columns)
# pass non-Index
newFrame = float_frame.reindex(list(datetime_series.index))
tm.assert_index_equal(newFrame.index, datetime_series.index)
# copy with no axes
result = float_frame.reindex()
assert_frame_equal(result, float_frame)
assert result is not float_frame
def test_pickle_preserve_name(self):
for n in [777, 777., 'name', datetime(2001, 11, 11), (1, 2)]:
unpickled = self._pickle_roundtrip_name(tm.makeTimeSeries(name=n))
assert unpickled.name == n
