def _init_dates(self, dates, freq):
if dates is None:
dates = self.data.row_labels
if dates is not None:
if (not datetools._is_datetime_index(dates) and
isinstance(self.data, data.PandasData)):
try:
if is_numeric_dtype(dates):
raise ValueError
dates = to_datetime(dates)
except ValueError:
raise ValueError("Given a pandas object and the index does "
"not contain dates")
if not freq:
try:
freq = datetools._infer_freq(dates)
except:
raise ValueError("Frequency inference failed. Use `freq` "
"keyword.")
if isinstance(dates[0], datetime.datetime):
dates = DatetimeIndex(dates)
else: # preserve PeriodIndex
dates = PeriodIndex(dates)
self.data.dates = dates
self.data.freq = freq
# Test for nanoseconds in early pandas versions
if freq is not None and _freq_to_pandas[freq].freqstr == 'N':
from distutils.version import LooseVersion
from pandas import __version__ as pd_version
if LooseVersion(pd_version) < '0.14':
raise NotImplementedError('Nanosecond index not available in'
' Pandas < 0.14')
def _init_dates(self, dates, freq):
if dates is None:
dates = self.data.row_labels
if dates is not None:
if (not datetools._is_datetime_index(dates) and
isinstance(self.data, data.PandasData)):
try:
if is_numeric_dtype(dates):
raise ValueError
dates = to_datetime(dates)
except ValueError:
raise ValueError("Given a pandas object and the index does "
"not contain dates")
if not freq:
try:
freq = datetools._infer_freq(dates)
except:
raise ValueError("Frequency inference failed. Use `freq` "
"keyword.")
if isinstance(dates[0], datetime.datetime):
dates = DatetimeIndex(dates)
else: # preserve PeriodIndex
dates = PeriodIndex(dates)
self.data.dates = dates
self.data.freq = freq
# Test for nanoseconds in early pandas versions
if freq is not None and _freq_to_pandas[freq].freqstr == 'N':
from distutils.version import LooseVersion
from pandas import __version__ as pd_version
if LooseVersion(pd_version) < '0.14':
raise NotImplementedError('Nanosecond index not available in'
' Pandas < 0.14')
def _init_dates(self, dates, freq):
if dates is None:
dates = self.data.row_labels
if dates is not None:
if (not datetools._is_datetime_index(dates) and
isinstance(self.data, data.PandasData)):
try:
if is_numeric_dtype(dates):
raise ValueError
dates = to_datetime(dates)
except ValueError:
raise ValueError("Given a pandas object and the index does "
"not contain dates")
if not freq:
try:
freq = datetools._infer_freq(dates)
except:
raise ValueError("Frequency inference failed. Use `freq` "
"keyword.")
if isinstance(dates[0], datetime.datetime):
dates = DatetimeIndex(dates)
else: # preserve PeriodIndex
dates = PeriodIndex(dates)
self.data.dates = dates
self.data.freq = freq
def _init_dates(self, dates, freq):
if dates is None:
dates = self.data.row_labels
if dates is not None:
if (not datetools._is_datetime_index(dates) and
isinstance(self.data, data.PandasData)):
try:
dates = to_datetime(dates)
except ValueError:
raise ValueError("Given a pandas object and the index does "
"not contain dates")
if not freq:
try:
freq = datetools._infer_freq(dates)
except:
raise ValueError("Frequency inference failed. Use `freq` "
"keyword.")
dates = Index(dates)
self.data.dates = dates
if freq:
try: #NOTE: Can drop this once we move to pandas >= 0.8.x
_freq_to_pandas[freq]
except:
raise ValueError("freq %s not understood" % freq)
self.data.freq = freq
def _init_dates(self, dates, freq):
if dates is None:
dates = self.data.row_labels
if dates is not None:
if (not datetools._is_datetime_index(dates)
and isinstance(self.data, data.PandasData)):
try:
if is_numeric_dtype(dates):
raise ValueError
dates = to_datetime(dates)
except ValueError:
raise ValueError(
"Given a pandas object and the index does "
"not contain dates")
if not freq:
try:
freq = datetools._infer_freq(dates)
except:
raise ValueError("Frequency inference failed. Use `freq` "
"keyword.")
if isinstance(dates[0], datetime.datetime):
dates = DatetimeIndex(dates)
else: # preserve PeriodIndex
dates = PeriodIndex(dates)
self.data.dates = dates
self.data.freq = freq
def _init_dates(self, dates, freq):
if dates is None:
dates = self._data.row_labels
if dates is not None:
try:
from scikits.timeseries import Date
if not isinstance(dates[0], (datetime.datetime,Date)):
raise ValueError("dates must be of type datetime or "
"scikits.timeseries.Date")
except ImportError:
if not isinstance(dates[0], (datetime.datetime)):
raise ValueError("dates must be of type datetime")
if not freq:
#if isinstance(dates, DateRange):
# freq = datetools.inferTimeRule(dates)
#elif isinstance(dates, TimeSeries):
# freq = dates.freqstr
try:
freq = datetools._infer_freq(dates)
except:
raise ValueError("Frequency inference failed. Use `freq` "
"keyword.")
dates = Index(dates)
self._data.dates = dates
self._data.freq = _check_freq(freq) #TODO: drop if can get info from dates
def test_period_index():
# tests 1285
if _pandas_08x:
from pandas import PeriodIndex
dates = PeriodIndex(start="1/1/1990", periods=20, freq="M")
npt.assert_(_infer_freq(dates) == "M")
def _check_period_index(x, freq="M"):
from pandas import PeriodIndex, DatetimeIndex
if not isinstance(x.index, (DatetimeIndex, PeriodIndex)):
raise ValueError("The index must be a DatetimeIndex or PeriodIndex")
from statsmodels.tsa.base.datetools import _infer_freq
inferred_freq = _infer_freq(x.index)
if not inferred_freq.startswith(freq):
raise ValueError("Expected frequency {}. Got {}".format(inferred_freq,
freq))
def _check_period_index(x, freq="M"):
try:
from pandas import PeriodIndex, DatetimeIndex
except ImportError: # not sure min. version
PeriodIndex = DatetimeIndex # HACK
from statsmodels.tools.data import _is_using_pandas
if not _is_using_pandas(x, None):
raise ValueError("x must be a pandas object")
if not isinstance(x.index, (DatetimeIndex, PeriodIndex)):
raise ValueError("The index must be a DatetimeIndex or PeriodIndex")
from statsmodels.tsa.base.datetools import _infer_freq
inferred_freq = _infer_freq(x.index)
if not inferred_freq.startswith(freq):
raise ValueError("Expected frequency {}. Got {}".format(inferred_freq,
freq))
def _check_period_index(x, freq="M"):
try:
from pandas import PeriodIndex, DatetimeIndex
except ImportError: # not sure min. version
PeriodIndex = DatetimeIndex # HACK
from statsmodels.tools.data import _is_using_pandas
if not _is_using_pandas(x, None):
raise ValueError("x must be a pandas object")
if not isinstance(x.index, (DatetimeIndex, PeriodIndex)):
raise ValueError("The index must be a DatetimeIndex or PeriodIndex")
from statsmodels.tsa.base.datetools import _infer_freq
inferred_freq = _infer_freq(x.index)
if not inferred_freq.startswith(freq):
raise ValueError("Expected frequency {}. Got {}".format(
inferred_freq, freq))
def _init_dates(self, dates, freq):
if dates is None:
dates = self.data.row_labels
if dates is not None:
if (not isinstance(dates[0], datetime.datetime) and
isinstance(self.data, data.PandasData)):
raise ValueError("Given a pandas object and the index does "
"not contain dates")
if not freq:
try:
freq = datetools._infer_freq(dates)
except:
raise ValueError("Frequency inference failed. Use `freq` "
"keyword.")
dates = Index(dates)
self.data.dates = dates
self.data.freq = _check_freq(freq) #TODO: drop if can get info from dates
def test_infer_freq():
d1 = datetime(2008, 12, 31)
d2 = datetime(2012, 9, 30)
if _pandas_08x:
b = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['B']).values
d = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['D']).values
w = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['W']).values
m = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['M']).values
a = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['A']).values
q = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['Q']).values
assert _infer_freq(w) == 'W-SUN'
assert _infer_freq(a) == 'A-DEC'
assert _infer_freq(q) == 'Q-DEC'
assert _infer_freq(w[:3]) == 'W-SUN'
assert _infer_freq(a[:3]) == 'A-DEC'
assert _infer_freq(q[:3]) == 'Q-DEC'
else:
from pandas import DateRange
b = DateRange(d1, d2, offset=_freq_to_pandas['B']).values
d = DateRange(d1, d2, offset=_freq_to_pandas['D']).values
w = DateRange(d1, d2, offset=_freq_to_pandas['W']).values
m = DateRange(d1, d2, offset=_freq_to_pandas['M']).values
a = DateRange(d1, d2, offset=_freq_to_pandas['A']).values
q = DateRange(d1, d2, offset=_freq_to_pandas['Q']).values
assert _infer_freq(w) == 'W'
assert _infer_freq(a) == 'A'
assert _infer_freq(q) == 'Q'
assert _infer_freq(w[:3]) == 'W'
assert _infer_freq(a[:3]) == 'A'
assert _infer_freq(q[:3]) == 'Q'
assert _infer_freq(b[2:5]) == 'B'
assert _infer_freq(b[:3]) == 'D'
assert _infer_freq(b) == 'B'
assert _infer_freq(d) == 'D'
assert _infer_freq(m) == 'M'
assert _infer_freq(d[:3]) == 'D'
assert _infer_freq(m[:3]) == 'M'
def test_infer_freq():
from pandas import DateRange
d1 = datetime(2008, 12, 31)
d2 = datetime(2012, 9, 30)
b = DateRange(d1, d2, offset=_freq_to_pandas['B']).values
d = DateRange(d1, d2, offset=_freq_to_pandas['D']).values
w = DateRange(d1, d2, offset=_freq_to_pandas['W']).values
m = DateRange(d1, d2, offset=_freq_to_pandas['M']).values
a = DateRange(d1, d2, offset=_freq_to_pandas['A']).values
q = DateRange(d1, d2, offset=_freq_to_pandas['Q']).values
assert _infer_freq(b[2:5]) == 'B'
assert _infer_freq(b[:3]) == 'D'
assert _infer_freq(b) == 'B'
assert _infer_freq(d) == 'D'
assert _infer_freq(w) == 'W'
assert _infer_freq(m) == 'M'
assert _infer_freq(a) == 'A'
assert _infer_freq(q) == 'Q'
assert _infer_freq(d[:3]) == 'D'
assert _infer_freq(w[:3]) == 'W'
assert _infer_freq(m[:3]) == 'M'
assert _infer_freq(a[:3]) == 'A'
assert _infer_freq(q[:3]) == 'Q'
def test_infer_freq():
d1 = datetime(2008, 12, 31)
d2 = datetime(2012, 9, 30)
if _pandas_08x:
b = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas["B"]).values
d = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas["D"]).values
w = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas["W"]).values
m = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas["M"]).values
a = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas["A"]).values
q = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas["Q"]).values
assert _infer_freq(w) == "W-SUN"
assert _infer_freq(a) == "A-DEC"
assert _infer_freq(q) == "Q-DEC"
assert _infer_freq(w[:3]) == "W-SUN"
assert _infer_freq(a[:3]) == "A-DEC"
assert _infer_freq(q[:3]) == "Q-DEC"
else:
from pandas import DateRange
b = DateRange(d1, d2, offset=_freq_to_pandas["B"]).values
d = DateRange(d1, d2, offset=_freq_to_pandas["D"]).values
w = DateRange(d1, d2, offset=_freq_to_pandas["W"]).values
m = DateRange(d1, d2, offset=_freq_to_pandas["M"]).values
a = DateRange(d1, d2, offset=_freq_to_pandas["A"]).values
q = DateRange(d1, d2, offset=_freq_to_pandas["Q"]).values
assert _infer_freq(w) == "W"
assert _infer_freq(a) == "A"
assert _infer_freq(q) == "Q"
assert _infer_freq(w[:3]) == "W"
assert _infer_freq(a[:3]) == "A"
assert _infer_freq(q[:3]) == "Q"
assert _infer_freq(b[2:5]) == "B"
assert _infer_freq(b[:3]) == "D"
assert _infer_freq(b) == "B"
assert _infer_freq(d) == "D"
assert _infer_freq(m) == "M"
assert _infer_freq(d[:3]) == "D"
assert _infer_freq(m[:3]) == "M"
def test_infer_freq():
d1 = datetime(2008, 12, 31)
d2 = datetime(2012, 9, 30)
if _pandas_08x:
b = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['B']).values
d = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['D']).values
w = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['W']).values
m = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['M']).values
a = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['A']).values
q = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['Q']).values
else:
from pandas import DateRange
b = DateRange(d1, d2, offset=_freq_to_pandas['B']).values
d = DateRange(d1, d2, offset=_freq_to_pandas['D']).values
w = DateRange(d1, d2, offset=_freq_to_pandas['W']).values
m = DateRange(d1, d2, offset=_freq_to_pandas['M']).values
a = DateRange(d1, d2, offset=_freq_to_pandas['A']).values
q = DateRange(d1, d2, offset=_freq_to_pandas['Q']).values
assert _infer_freq(b[2:5]) == 'B'
assert _infer_freq(b[:3]) == 'D'
assert _infer_freq(b) == 'B'
assert _infer_freq(d) == 'D'
assert _infer_freq(w) == 'W'
assert _infer_freq(m) == 'M'
assert _infer_freq(a) == 'A'
assert _infer_freq(q) == 'Q'
assert _infer_freq(d[:3]) == 'D'
assert _infer_freq(w[:3]) == 'W'
assert _infer_freq(m[:3]) == 'M'
assert _infer_freq(a[:3]) == 'A'
assert _infer_freq(q[:3]) == 'Q'
def test_period_index():
# tests 1285
from pandas import PeriodIndex
dates = PeriodIndex(start="1/1/1990", periods=20, freq="M")
npt.assert_(_infer_freq(dates) == "M")
def test_infer_freq():
d1 = datetime(2008, 12, 31)
d2 = datetime(2012, 9, 30)
b = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['B']).values
d = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['D']).values
w = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['W']).values
m = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['M']).values
a = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['A']).values
q = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['Q']).values
npt.assert_(_infer_freq(w) == 'W-SUN')
npt.assert_(_infer_freq(a) == 'A-DEC')
npt.assert_(_infer_freq(q) == 'Q-DEC')
npt.assert_(_infer_freq(w[:3]) == 'W-SUN')
npt.assert_(_infer_freq(a[:3]) == 'A-DEC')
npt.assert_(_infer_freq(q[:3]) == 'Q-DEC')
npt.assert_(_infer_freq(b[2:5]) == 'B')
npt.assert_(_infer_freq(b[:3]) == 'D')
npt.assert_(_infer_freq(b) == 'B')
npt.assert_(_infer_freq(d) == 'D')
npt.assert_(_infer_freq(m) == 'M')
npt.assert_(_infer_freq(d[:3]) == 'D')
npt.assert_(_infer_freq(m[:3]) == 'M')
def test_infer_freq():
from pandas import DateRange
d1 = datetime(2008, 12, 31)
d2 = datetime(2012, 9, 30)
b = DateRange(d1, d2, offset=_freq_to_pandas["B"]).values
d = DateRange(d1, d2, offset=_freq_to_pandas["D"]).values
w = DateRange(d1, d2, offset=_freq_to_pandas["W"]).values
m = DateRange(d1, d2, offset=_freq_to_pandas["M"]).values
a = DateRange(d1, d2, offset=_freq_to_pandas["A"]).values
q = DateRange(d1, d2, offset=_freq_to_pandas["Q"]).values
assert _infer_freq(b[2:5]) == "B"
assert _infer_freq(b[:3]) == "D"
assert _infer_freq(b) == "B"
assert _infer_freq(d) == "D"
assert _infer_freq(w) == "W"
assert _infer_freq(m) == "M"
assert _infer_freq(a) == "A"
assert _infer_freq(q) == "Q"
assert _infer_freq(d[:3]) == "D"
assert _infer_freq(w[:3]) == "W"
assert _infer_freq(m[:3]) == "M"
assert _infer_freq(a[:3]) == "A"
assert _infer_freq(q[:3]) == "Q"
def test_infer_freq():
from pandas import DateRange
d1 = datetime(2008, 12, 31)
d2 = datetime(2012, 9, 30)
b = DateRange(d1, d2, offset=_freq_to_pandas['B']).values
d = DateRange(d1, d2, offset=_freq_to_pandas['D']).values
w = DateRange(d1, d2, offset=_freq_to_pandas['W']).values
m = DateRange(d1, d2, offset=_freq_to_pandas['M']).values
a = DateRange(d1, d2, offset=_freq_to_pandas['A']).values
q = DateRange(d1, d2, offset=_freq_to_pandas['Q']).values
npt.assert_string_equal(_infer_freq(b), 'B')
npt.assert_string_equal(_infer_freq(d), 'D')
npt.assert_string_equal(_infer_freq(w), 'W')
npt.assert_string_equal(_infer_freq(m), 'M')
npt.assert_string_equal(_infer_freq(a), 'A')
npt.assert_string_equal(_infer_freq(q), 'Q')
npt.assert_string_equal(_infer_freq(b[2:4]), 'B')
npt.assert_string_equal(_infer_freq(b[:2]), 'D')
npt.assert_string_equal(_infer_freq(d[:2]), 'D')
npt.assert_string_equal(_infer_freq(w[:2]), 'W')
npt.assert_string_equal(_infer_freq(m[:2]), 'M')
npt.assert_string_equal(_infer_freq(a[:2]), 'A')
npt.assert_string_equal(_infer_freq(q[:2]), 'Q')
def test_period_index():
dates = PeriodIndex(start="1/1/1990", periods=20, freq="M")
npt.assert_(_infer_freq(dates) == "M")
