def test_supermeta(self):
"""
Test that supermeta metaclass acts like a super parent
to both UserSeries and UserFrame
"""
class CommonBase(composition.PandasSuperMeta):
"""
Test common base
"""
_bob = object()
@property
def bob(self):
return self._bob
class CommonSeries(UserSeries, metaclass=CommonBase):
pass
class CommonFrame(UserFrame, metaclass=CommonBase):
pass
bob = CommonBase._bob
s = CommonSeries(list(range(10)))
assert s.ix[3] == 3
tm.assert_almost_equal(s, list(range(10)))
assert s.bob is bob
s._bob = 123
assert s.bob == 123
df = tm.makeDataFrame()
fr = CommonFrame(df)
tm.assert_almost_equal(fr.values, df.values)
assert fr.bob is bob
assert fr.tail().bob is bob
def test_supermeta(self):
"""
Test that supermeta metaclass acts like a super parent
to both UserSeries and UserFrame
"""
class CommonBase(composition.PandasSuperMeta):
"""
Test common base
"""
_bob = object()
@property
def bob(self):
return self._bob
class CommonSeries(UserSeries):
__metaclass__ = CommonBase
class CommonFrame(UserFrame):
__metaclass__ = CommonBase
bob = CommonBase._bob
s = CommonSeries(range(10))
assert s.ix[3] == 3
tm.assert_almost_equal(s, range(10))
assert s.bob is bob
s._bob = 123
assert s.bob == 123
df = tm.makeDataFrame()
fr = CommonFrame(df)
tm.assert_almost_equal(fr, df)
assert fr.bob is bob
assert fr.tail().bob is bob
def test_convert_datetime(self):
ind = pd.date_range(start='1/1/2002', end='12/30/2008', freq="30min")
ri = conv.convert_datetime_index_num(ind)
tm.assert_almost_equal(ri[0], ind.asi8[0] / 1E9)
assert ri.do_slot('tzone')[0] == 'UTC'
# convert back to datetime
conv_dates = pd.Series(ri).astype(np.dtype('M8[s]'))
tm.assert_almost_equal(conv_dates, ind)
def test_convert_datetime(self):
ind = pd.date_range(start='1/1/2002', end='12/30/2008', freq="30min")
ri = conv.convert_datetime_index_num(ind)
tm.assert_almost_equal(ri[0], ind.asi8[0] / 1E9)
assert ri.do_slot('tzone')[0] == 'UTC'
# convert back to datetime
conv_dates = pd.Series(ri).astype(np.dtype('M8[s]'))
tm.assert_almost_equal(conv_dates, ind)
def test_dshift_fill_value(self):
# float
test = df.dshift(1, fill_value=-100)
correct = df.shift(1).fillna(-100)
tm.assert_almost_equal(test.values, correct.values)
# int
intdf = (df * 100).astype(int)
test = intdf.dshift(1, fill_value=-100)
correct = intdf.shift(1).fillna(-100)
tm.assert_almost_equal(test.values, correct.values)
def test_dshift_fill_value(self):
# float
test = df.dshift(1, fill_value=-100)
correct = df.shift(1).fillna(-100)
tm.assert_almost_equal(test.values, correct.values)
# int
intdf = (df * 100).astype(int)
test = intdf.dshift(1, fill_value=-100)
correct = intdf.shift(1).fillna(-100)
tm.assert_almost_equal(test.values, correct.values)
def test_dshift_float(self):
"""
Since float is nan-able. The simple call should give the
same output
"""
test = df.dshift(1)
correct = df.shift(1)
tm.assert_almost_equal(test.values, correct.values)
test = df.dshift(-2)
correct = df.shift(-2)
tm.assert_almost_equal(test.values, correct.values)
def test_dshift_float(self):
"""
Since float is nan-able. The simple call should give the
same output
"""
test = df.dshift(1)
correct = df.shift(1)
tm.assert_almost_equal(test.values, correct.values)
test = df.dshift(-2)
correct = df.shift(-2)
tm.assert_almost_equal(test.values, correct.values)
def test_dshift_raw(self):
# bool
bf = df > 0
test = bf.dshift(1, raw=True)
correct = bf.shift(1).fillna(False).astype(float)
assert type(test) is np.ndarray
tm.assert_almost_equal(test, correct.values)
# float
test = df.dshift(1, raw=True)
correct = df.shift(1)
assert type(test) is np.ndarray
tm.assert_almost_equal(test, correct.values)
def test_dshift_raw(self):
# bool
bf = df > 0
test = bf.dshift(1, raw=True)
correct = bf.shift(1).fillna(False).astype(float)
assert type(test) is np.ndarray
tm.assert_almost_equal(test, correct.values)
# float
test = df.dshift(1, raw=True)
correct = df.shift(1)
assert type(test) is np.ndarray
tm.assert_almost_equal(test, correct.values)
def test_dshift_int(self):
"""
int has no nan.
"""
intdf = (df * 100).astype(int)
test = intdf.dshift(1)
correct = intdf.shift(1).fillna(-1).astype(int)
assert test.dtypes.unique()[0] == int
assert test.dtypes.nunique() == 1
tm.assert_almost_equal(test.values, correct.values)
test = intdf.dshift(-2)
correct = intdf.shift(-2).fillna(-1).astype(int)
assert test.dtypes.unique()[0] == int
assert test.dtypes.nunique() == 1
tm.assert_almost_equal(test.values, correct.values)
def test_dshift_bool(self):
"""
bool has no nan.
"""
bf = df > 0
test = bf.dshift(1)
correct = bf.shift(1).fillna(False).astype(bool)
assert test.dtypes.unique()[0] == bool
assert test.dtypes.nunique() == 1
tm.assert_almost_equal(test.values, correct.values)
test = bf.dshift(-2)
correct = bf.shift(-2).fillna(False).astype(bool)
assert test.dtypes.unique()[0] == bool
assert test.dtypes.nunique() == 1
tm.assert_almost_equal(test.values, correct.values)
def test_dshift_bool(self):
"""
bool has no nan.
"""
bf = df > 0
test = bf.dshift(1)
correct = bf.shift(1).fillna(False).astype(bool)
assert test.dtypes.unique()[0] == bool
assert test.dtypes.nunique() == 1
tm.assert_almost_equal(test.values, correct.values)
test = bf.dshift(-2)
correct = bf.shift(-2).fillna(False).astype(bool)
assert test.dtypes.unique()[0] == bool
assert test.dtypes.nunique() == 1
tm.assert_almost_equal(test.values, correct.values)
def test_dshift_int(self):
"""
int has no nan.
"""
intdf = (df * 100).astype(int)
test = intdf.dshift(1)
correct = intdf.shift(1).fillna(-1).astype(int)
assert test.dtypes.unique()[0] == int
assert test.dtypes.nunique() == 1
tm.assert_almost_equal(test.values, correct.values)
test = intdf.dshift(-2)
correct = intdf.shift(-2).fillna(-1).astype(int)
assert test.dtypes.unique()[0] == int
assert test.dtypes.nunique() == 1
tm.assert_almost_equal(test.values, correct.values)
def test_convert_posixct_to_index(self):
ind = pd.date_range(start='1/1/2002', end='12/30/2008', freq="30min")
ri = conv.convert_datetime_index_num(ind)
# test converting back 'UTC'
dt = conv.convert_posixct_to_index(ri)
tm.assert_almost_equal(dt, ind,)
# test 'US/Eastern'
# At one point this failed due to DatetimeIndex being
# given a tz. DatetimeIndex needs to be initialied to UTC
# and then converted
ri.do_slot_assign('tzone', StrSexpVector(['US/Eastern']))
dt = conv.convert_posixct_to_index(ri)
assert dt.tz.zone == 'US/Eastern'
assert ind.tz is None
tm.assert_almost_equal(dt, ind)
ind = ind.tz_localize('UTC')
est_ind = ind.tz_convert('US/Eastern')
est_ri = conv.convert_datetime_index_num(est_ind)
assert est_ri.do_slot('tzone')[0] == 'US/Eastern'
est_dt = conv.convert_posixct_to_index(est_ri)
tm.assert_almost_equal(est_dt, est_ind)
assert est_dt.tz.zone == 'US/Eastern'
def test_convert_posixct_to_index(self):
ind = pd.date_range(start='1/1/2002', end='12/30/2008', freq="30min")
ri = conv.convert_datetime_index_num(ind)
# test converting back 'UTC'
dt = conv.convert_posixct_to_index(ri)
tm.assert_almost_equal(
dt,
ind,
)
# test 'US/Eastern'
# At one point this failed due to DatetimeIndex being
# given a tz. DatetimeIndex needs to be initialied to UTC
# and then converted
ri.do_slot_assign('tzone', StrSexpVector(['US/Eastern']))
dt = conv.convert_posixct_to_index(ri)
assert dt.tz.zone == 'US/Eastern'
assert ind.tz is None
tm.assert_almost_equal(dt, ind)
ind = ind.tz_localize('UTC')
est_ind = ind.tz_convert('US/Eastern')
est_ri = conv.convert_datetime_index_num(est_ind)
assert est_ri.do_slot('tzone')[0] == 'US/Eastern'
est_dt = conv.convert_posixct_to_index(est_ri)
tm.assert_almost_equal(est_dt, est_ind)
assert est_dt.tz.zone == 'US/Eastern'
def test_level_wrapper_mi(self):
"""
Test level wrapper against MultiIndex
"""
mi = pd.MultiIndex.from_tuples(sets)
df = pd.DataFrame(np.random.randn(N, N), index=ind)
df.columns = mi
df.columns.names = ['limit', 'stop', 'target']
m = MultiIndexGetter(df, attr='columns')
lw = df.columns.lev.limit
# test some ops
test = lw == 0
correct = mi.get_level_values('limit') == 0
tm.assert_almost_equal(test, correct)
test = lw > 1
correct = mi.get_level_values('limit') > 1
tm.assert_almost_equal(test, correct)
# test getitem
assert lw[0] == 0
assert lw[1] == 1
def test_level_wrapper_mi(self):
"""
Test level wrapper against MultiIndex
"""
mi = pd.MultiIndex.from_tuples(sets)
df = pd.DataFrame(np.random.randn(N, N), index=ind)
df.columns = mi
df.columns.names = ['limit', 'stop', 'target']
m = MultiIndexGetter(df, attr='columns')
lw = df.columns.lev.limit
# test some ops
test = lw == 0
correct = mi.get_level_values('limit') == 0
tm.assert_almost_equal(test, correct)
test = lw > 1
correct = mi.get_level_values('limit') > 1
tm.assert_almost_equal(test, correct)
# test getitem
assert lw[0] == 0
assert lw[1] == 1
def test_save(self):
with TemporaryDirectory() as td:
filepath = os.path.join(td, "test")
df = pd.DataFrame({"test": list(range(10))})
table = pddb.PandasTable(filepath)
table._init_df(df)
# save to string
data = tm.TestStringIO()
table.save(data)
# PandasTable saves as df
data.seek(0)
new_table = pickle.loads(data.read())
assert tm.assert_almost_equal(new_table.values, df.values)
data.free()
# save to disk
table.save()
pandas_table = pddb.PandasTable(filepath)
assert tm.assert_almost_equal(pandas_table._df.values, df.values)
def test_save(self):
with TemporaryDirectory() as td:
filepath = os.path.join(td, 'test')
df = pd.DataFrame({'test':list(range(10))})
table = pddb.PandasTable(filepath)
table._init_df(df)
# save to string
data = tm.TestStringIO()
table.save(data)
# PandasTable saves as df
data.seek(0)
new_table = pickle.loads(data.read())
assert tm.assert_almost_equal(new_table.values, df.values)
data.free()
# save to disk
table.save()
pandas_table = pddb.PandasTable(filepath)
assert tm.assert_almost_equal(pandas_table._df.values, df.values)
def test_init_df(self):
with TemporaryDirectory() as td:
filepath = os.path.join(td, "test")
df = pd.DataFrame({"test": range(10)})
table = pddb.PandasTable(filepath)
table.init_df(df)
assert df is table._df
data = tm.TestStringIO(pickle.dumps(df))
table = pddb.PandasTable(filepath)
table._init_df(data)
assert tm.assert_almost_equal(df, table._df)
def test_init_df(self):
with TemporaryDirectory() as td:
filepath = os.path.join(td, 'test')
df = pd.DataFrame({'test':range(10)})
table = pddb.PandasTable(filepath)
table.init_df(df)
assert df is table._df
data = tm.TestStringIO(pickle.dumps(df))
table = pddb.PandasTable(filepath)
table._init_df(data)
assert tm.assert_almost_equal(df, table._df)
def test_save(self):
with TemporaryDirectory() as td:
filepath = os.path.join(td, "test")
df = pd.DataFrame({"test": range(10)})
table = pddb.PandasTable(filepath)
table._init_df(df)
data = tm.TestStringIO()
table.save(data)
data.seek(0)
new_table = pickle.loads(data.read())
assert tm.assert_almost_equal(new_table._df, df)
data.free()
def test_save(self):
with TemporaryDirectory() as td:
filepath = os.path.join(td, 'test')
df = pd.DataFrame({'test':range(10)})
table = pddb.PandasTable(filepath)
table._init_df(df)
data = tm.TestStringIO()
table.save(data)
data.seek(0)
new_table = pickle.loads(data.read())
assert tm.assert_almost_equal(new_table._df, df)
data.free()