def test_mixed_freq_regular_first(self):
import matplotlib.pyplot as plt
s1 = tm.makeTimeSeries()
s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]]
ax = s1.plot()
ax2 = s2.plot(style='g')
lines = ax2.get_lines()
idx1 = PeriodIndex(lines[0].get_xdata())
idx2 = PeriodIndex(lines[1].get_xdata())
self.assertTrue(idx1.equals(s1.index.to_period('B')))
self.assertTrue(idx2.equals(s2.index.to_period('B')))
left, right = ax2.get_xlim()
pidx = s1.index.to_period()
self.assertEqual(left, pidx[0].ordinal)
self.assertEqual(right, pidx[-1].ordinal)
def _resample_periods(self, obj):
axlabels = obj._get_axis(self.axis)
if len(axlabels) == 0:
new_index = PeriodIndex(data=[], freq=self.freq)
return obj.reindex(new_index)
else:
start = axlabels[0].asfreq(self.freq, how=self.convention)
end = axlabels[-1].asfreq(self.freq, how='end')
new_index = period_range(start, end, freq=self.freq)
# Start vs. end of period
memb = axlabels.asfreq(self.freq, how=self.convention)
if is_subperiod(axlabels.freq, self.freq) or self.how is not None:
# Downsampling
rng = np.arange(memb.values[0], memb.values[-1] + 1)
bins = memb.searchsorted(rng, side='right')
grouper = BinGrouper(bins, new_index)
grouped = obj.groupby(grouper, axis=self.axis)
return grouped.aggregate(self._agg_method)
elif is_superperiod(axlabels.freq, self.freq):
# Get the fill indexer
indexer = memb.get_indexer(new_index,
method=self.fill_method,
limit=self.limit)
return _take_new_index(obj, indexer, new_index, axis=self.axis)
else:
raise ValueError('Frequency %s cannot be resampled to %s' %
(axlabels.freq, self.freq))
def maybe_to_datetimelike(data, copy=False):
"""
return a DelegatedClass of a Series that is datetimelike
(e.g. datetime64[ns],timedelta64[ns] dtype or a Series of Periods)
raise TypeError if this is not possible.
Parameters
----------
data : Series
copy : boolean, default False
copy the input data
Returns
-------
DelegatedClass
"""
from pandas import Series
if not isinstance(data, Series):
raise TypeError("cannot convert an object of type {0} to a datetimelike index".format(type(data)))
index = data.index
if issubclass(data.dtype.type, np.datetime64):
return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer'), index)
elif issubclass(data.dtype.type, np.timedelta64):
return TimedeltaProperties(TimedeltaIndex(data, copy=copy, freq='infer'), index)
else:
if is_period_arraylike(data):
return PeriodProperties(PeriodIndex(data, copy=copy), index)
if is_datetime_arraylike(data):
return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer'), index)
raise TypeError("cannot convert an object of type {0} to a datetimelike index".format(type(data)))
def _get_time_period_bins(self, axis):
if not (isinstance(axis, DatetimeIndex)):
raise AssertionError()
if len(axis) == 0:
binner = labels = PeriodIndex(data=[], freq=self.freq)
return binner, [], labels
labels = binner = PeriodIndex(start=axis[0],
end=axis[-1],
freq=self.freq)
end_stamps = (labels + 1).asfreq('D', 's').to_timestamp()
bins = axis.searchsorted(end_stamps, side='left')
return binner, bins, labels
def _get_time_period_bins(self, axis):
if not isinstance(axis, DatetimeIndex):
raise TypeError('axis must be a DatetimeIndex, but got '
'an instance of %r' % type(axis).__name__)
if not len(axis):
binner = labels = PeriodIndex(data=[], freq=self.freq)
return binner, [], labels
labels = binner = PeriodIndex(start=axis[0],
end=axis[-1],
freq=self.freq)
end_stamps = (labels + 1).asfreq('D', 's').to_timestamp()
bins = axis.searchsorted(end_stamps, side='left')
return binner, bins, labels
def test_business_freq(self):
import matplotlib.pyplot as plt # noqa
bts = tm.makePeriodSeries()
ax = bts.plot()
self.assertEqual(ax.get_lines()[0].get_xydata()[0, 0],
bts.index[0].ordinal)
idx = ax.get_lines()[0].get_xdata()
self.assertEqual(PeriodIndex(data=idx).freqstr, 'B')
def test_mixed_freq_regular_first_df(self):
# GH 9852
import matplotlib.pyplot as plt # noqa
s1 = tm.makeTimeSeries().to_frame()
s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15], :]
ax = s1.plot()
ax2 = s2.plot(style="g", ax=ax)
lines = ax2.get_lines()
idx1 = PeriodIndex(lines[0].get_xdata())
idx2 = PeriodIndex(lines[1].get_xdata())
self.assertTrue(idx1.equals(s1.index.to_period("B")))
self.assertTrue(idx2.equals(s2.index.to_period("B")))
left, right = ax2.get_xlim()
pidx = s1.index.to_period()
self.assertEqual(left, pidx[0].ordinal)
self.assertEqual(right, pidx[-1].ordinal)
def test_business_freq(self):
import matplotlib.pyplot as plt
plt.close('all')
bts = tm.makePeriodSeries()
ax = bts.plot()
self.assert_(ax.get_lines()[0].get_xydata()[0, 0],
bts.index[0].ordinal)
idx = ax.get_lines()[0].get_xdata()
self.assert_(PeriodIndex(data=idx).freqstr == 'B')
def test_to_weekly_resampling(self):
idxh = date_range('1/1/1999', periods=52, freq='W')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
high.plot()
ax = low.plot()
for l in ax.get_lines():
self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, idxh.freq)
# tsplot
from pandas.tseries.plotting import tsplot
import matplotlib.pyplot as plt
tsplot(high, plt.Axes.plot)
lines = tsplot(low, plt.Axes.plot)
for l in lines:
self.assertTrue(PeriodIndex(data=l.get_xdata()).freq, idxh.freq)
def test_mixed_freq_hf_first(self):
idxh = date_range('1/1/1999', periods=365, freq='D')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
high.plot()
ax = low.plot()
for l in ax.get_lines():
self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'D')
def _get_time_period_bins(self, axis):
assert (isinstance(axis, DatetimeIndex))
if len(axis) == 0:
binner = labels = PeriodIndex(data=[], freq=self.freq)
return binner, [], labels
labels = binner = PeriodIndex(start=axis[0],
end=axis[-1],
freq=self.freq)
end_stamps = (labels + 1).asfreq('D', 's').to_timestamp()
bins = axis.searchsorted(end_stamps, side='left')
if bins[-1] < len(axis):
bins = np.concatenate([bins, [len(axis)]])
return binner, bins, labels
def test_from_weekly_resampling(self):
idxh = date_range('1/1/1999', periods=52, freq='W')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
low.plot()
ax = high.plot()
for l in ax.get_lines():
self.assert_(PeriodIndex(data=l.get_xdata()).freq.startswith('W'))
def test_mixed_freq_second_millisecond(self):
# GH 7772, GH 7760
idxh = date_range('2014-07-01 09:00', freq='S', periods=50)
idxl = date_range('2014-07-01 09:00', freq='100L', periods=500)
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
# high to low
high.plot()
ax = low.plot()
self.assertEqual(len(ax.get_lines()), 2)
for l in ax.get_lines():
self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'L')
tm.close()
# low to high
low.plot()
ax = high.plot()
self.assertEqual(len(ax.get_lines()), 2)
for l in ax.get_lines():
self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'L')
def test_mixed_freq_hf_first(self):
import matplotlib.pyplot as plt
plt.close('all')
idxh = date_range('1/1/1999', periods=365, freq='D')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
high.plot()
ax = low.plot()
for l in ax.get_lines():
self.assert_(PeriodIndex(data=l.get_xdata()).freq == 'D')
def test_to_weekly_resampling(self):
import matplotlib.pyplot as plt
plt.close('all')
idxh = date_range('1/1/1999', periods=52, freq='W')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
high.plot()
ax = low.plot()
for l in ax.get_lines():
self.assert_(PeriodIndex(data=l.get_xdata()).freq.startswith('W'))
def _value_counts_arraylike(values, dropna=True):
is_datetimetz = com.is_datetimetz(values)
is_period = (isinstance(values, gt.ABCPeriodIndex) or
com.is_period_arraylike(values))
orig = values
from pandas.core.series import Series
values = Series(values).values
dtype = values.dtype
if com.is_datetime_or_timedelta_dtype(dtype) or is_period:
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
if is_period:
values = PeriodIndex(values)
freq = values.freq
values = values.view(np.int64)
keys, counts = htable.value_count_scalar64(values, dropna)
if dropna:
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
# convert the keys back to the dtype we came in
keys = keys.astype(dtype)
# dtype handling
if is_datetimetz:
if isinstance(orig, gt.ABCDatetimeIndex):
tz = orig.tz
else:
tz = orig.dt.tz
keys = DatetimeIndex._simple_new(keys, tz=tz)
if is_period:
keys = PeriodIndex._simple_new(keys, freq=freq)
elif com.is_integer_dtype(dtype):
values = com._ensure_int64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
elif com.is_float_dtype(dtype):
values = com._ensure_float64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
else:
values = com._ensure_object(values)
mask = com.isnull(values)
keys, counts = htable.value_count_object(values, mask)
if not dropna and mask.any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
return keys, counts
def test_business_freq_convert(self):
n = tm.N
tm.N = 300
bts = tm.makeTimeSeries().asfreq('BM')
tm.N = n
ts = bts.to_period('M')
ax = bts.plot()
self.assertEqual(ax.get_lines()[0].get_xydata()[0, 0],
ts.index[0].ordinal)
idx = ax.get_lines()[0].get_xdata()
self.assertEqual(PeriodIndex(data=idx).freqstr, 'M')
def _get_time_period_bins(self, ax):
if not isinstance(ax, DatetimeIndex):
raise TypeError('axis must be a DatetimeIndex, but got '
'an instance of %r' % type(ax).__name__)
if not len(ax):
binner = labels = PeriodIndex(
data=[], freq=self.freq, name=ax.name)
return binner, [], labels
labels = binner = PeriodIndex(start=ax[0],
end=ax[-1],
freq=self.freq,
name=ax.name)
end_stamps = (labels + 1).asfreq(self.freq, 's').to_timestamp()
if ax.tzinfo:
end_stamps = end_stamps.tz_localize(ax.tzinfo)
bins = ax.searchsorted(end_stamps, side='left')
return binner, bins, labels
def test_business_freq_convert(self):
import matplotlib.pyplot as plt
plt.close('all')
n = tm.N
tm.N = 300
bts = tm.makeTimeSeries().asfreq('BM')
tm.N = n
ts = bts.to_period('M')
ax = bts.plot()
self.assert_(ax.get_lines()[0].get_xydata()[0, 0], ts.index[0].ordinal)
idx = ax.get_lines()[0].get_xdata()
self.assert_(PeriodIndex(data=idx).freqstr == 'M')
def _value_counts_arraylike(values, dropna=True):
is_datetimetz_type = is_datetimetz(values)
is_period_type = (is_period_dtype(values) or
is_period_arraylike(values))
orig = values
from pandas.core.series import Series
values = Series(values).values
dtype = values.dtype
if needs_i8_conversion(dtype) or is_period_type:
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
if is_period_type:
# values may be an object
values = PeriodIndex(values)
freq = values.freq
values = values.view(np.int64)
keys, counts = htable.value_count_int64(values, dropna)
if dropna:
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
# convert the keys back to the dtype we came in
keys = keys.astype(dtype)
# dtype handling
if is_datetimetz_type:
keys = DatetimeIndex._simple_new(keys, tz=orig.dtype.tz)
if is_period_type:
keys = PeriodIndex._simple_new(keys, freq=freq)
elif is_integer_dtype(dtype):
values = _ensure_int64(values)
keys, counts = htable.value_count_int64(values, dropna)
elif is_float_dtype(dtype):
values = _ensure_float64(values)
keys, counts = htable.value_count_float64(values, dropna)
else:
values = _ensure_object(values)
mask = isnull(values)
keys, counts = htable.value_count_object(values, mask)
if not dropna and mask.any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
return keys, counts
def _get_new_index(self):
""" return our new index """
ax = self.ax
obj = self._selected_obj
if len(ax) == 0:
new_index = PeriodIndex(data=[], freq=self.freq)
return obj.reindex(new_index)
start = ax[0].asfreq(self.freq, how=self.convention)
end = ax[-1].asfreq(self.freq, how='end')
return period_range(start, end, freq=self.freq)
def maybe_to_datetimelike(data, copy=False):
"""
return a DelegatedClass of a Series that is datetimelike
(e.g. datetime64[ns],timedelta64[ns] dtype or a Series of Periods)
raise TypeError if this is not possible.
Parameters
----------
data : Series
copy : boolean, default False
copy the input data
Returns
-------
DelegatedClass
"""
from pandas import Series
if not isinstance(data, Series):
raise TypeError("cannot convert an object of type {0} to a "
"datetimelike index".format(type(data)))
index = data.index
name = data.name
orig = data if is_categorical_dtype(data) else None
if orig is not None:
data = orig.values.categories
if is_datetime64_dtype(data.dtype):
return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer'),
index, name=name, orig=orig)
elif is_datetime64tz_dtype(data.dtype):
return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer',
ambiguous='infer'),
index, data.name, orig=orig)
elif is_timedelta64_dtype(data.dtype):
return TimedeltaProperties(TimedeltaIndex(data, copy=copy,
freq='infer'), index,
name=name, orig=orig)
else:
if is_period_arraylike(data):
return PeriodProperties(PeriodIndex(data, copy=copy), index,
name=name, orig=orig)
if is_datetime_arraylike(data):
return DatetimeProperties(DatetimeIndex(data, copy=copy,
freq='infer'), index,
name=name, orig=orig)
raise TypeError("cannot convert an object of type {0} to a "
"datetimelike index".format(type(data)))
def test_periodindex(self):
idx1 = PeriodIndex(
['2014-01', '2014-01', '2014-02', '2014-02', '2014-03', '2014-03'],
freq='M')
cat1 = Categorical.from_array(idx1)
exp_arr = np.array([0, 0, 1, 1, 2, 2])
exp_idx = PeriodIndex(['2014-01', '2014-02', '2014-03'], freq='M')
self.assert_numpy_array_equal(cat1.labels, exp_arr)
self.assert_(cat1.levels.equals(exp_idx))
idx2 = PeriodIndex(
['2014-03', '2014-03', '2014-02', '2014-01', '2014-03', '2014-01'],
freq='M')
cat2 = Categorical.from_array(idx2)
exp_arr = np.array([2, 2, 1, 0, 2, 0])
self.assert_numpy_array_equal(cat2.labels, exp_arr)
self.assert_(cat2.levels.equals(exp_idx))
idx3 = PeriodIndex([
'2013-12', '2013-11', '2013-10', '2013-09', '2013-08', '2013-07',
'2013-05'
],
freq='M')
cat3 = Categorical.from_array(idx3)
exp_arr = np.array([6, 5, 4, 3, 2, 1, 0])
exp_idx = PeriodIndex([
'2013-05', '2013-07', '2013-08', '2013-09', '2013-10', '2013-11',
'2013-12'
],
freq='M')
self.assert_numpy_array_equal(cat3.labels, exp_arr)
self.assert_(cat3.levels.equals(exp_idx))
def test_from_weekly_resampling(self):
idxh = date_range('1/1/1999', periods=52, freq='W')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
low.plot()
ax = high.plot()
expected_h = idxh.to_period().asi8
expected_l = np.array([
1514, 1519, 1523, 1527, 1531, 1536, 1540, 1544, 1549, 1553, 1558,
1562
])
for l in ax.get_lines():
self.assertTrue(
PeriodIndex(data=l.get_xdata()).freq.startswith('W'))
xdata = l.get_xdata(orig=False)
if len(xdata) == 12: # idxl lines
self.assert_numpy_array_equal(xdata, expected_l)
else:
self.assert_numpy_array_equal(xdata, expected_h)
tm.close()
# tsplot
from pandas.tseries.plotting import tsplot
import matplotlib.pyplot as plt
tsplot(low, plt.Axes.plot)
lines = tsplot(high, plt.Axes.plot)
for l in lines:
self.assertTrue(
PeriodIndex(data=l.get_xdata()).freq.startswith('W'))
xdata = l.get_xdata(orig=False)
if len(xdata) == 12: # idxl lines
self.assert_numpy_array_equal(xdata, expected_l)
else:
self.assert_numpy_array_equal(xdata, expected_h)
def test_mixed_freq_lf_first(self):
import matplotlib.pyplot as plt
plt.close('all')
idxh = date_range('1/1/1999', periods=365, freq='D')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
low.plot(legend=True)
ax = high.plot(legend=True)
for l in ax.get_lines():
self.assert_(PeriodIndex(data=l.get_xdata()).freq == 'D')
leg = ax.get_legend()
self.assert_(len(leg.texts) == 2)
plt.close('all')
idxh = date_range('1/1/1999', periods=240, freq='T')
idxl = date_range('1/1/1999', periods=4, freq='H')
high = Series(np.random.randn(len(idxh)), idxh)
low = Series(np.random.randn(len(idxl)), idxl)
low.plot()
ax = high.plot()
for l in ax.get_lines():
self.assert_(PeriodIndex(data=l.get_xdata()).freq == 'T')
def to_period(self, freq=None):
"""
Cast to PeriodIndex at a particular frequency
"""
from pandas.tseries.period import PeriodIndex
if self.freq is None and freq is None:
msg = "You must pass a freq argument as current index has none."
raise ValueError(msg)
if freq is None:
freq = get_period_alias(self.freqstr)
return PeriodIndex(self.values, freq=freq)
def convert(values, units, axis):
if not hasattr(axis, 'freq'):
raise TypeError('Axis must have `freq` set to convert to Periods')
valid_types = (str, datetime, Period, pydt.date, pydt.time)
if (isinstance(values, valid_types) or com.is_integer(values)
or com.is_float(values)):
return get_datevalue(values, axis.freq)
if isinstance(values, PeriodIndex):
return values.asfreq(axis.freq).values
if isinstance(values, Index):
return values.map(lambda x: get_datevalue(x, axis.freq))
if isinstance(values, (list, tuple, np.ndarray, Index)):
return PeriodIndex(values, freq=axis.freq).values
return values
def test_resample_fill_missing(self):
rng = PeriodIndex([2000, 2005, 2007, 2009], freq='A')
s = TimeSeries(np.random.randn(4), index=rng)
stamps = s.to_timestamp()
filled = s.resample('A')
expected = stamps.resample('A').to_period('A')
assert_series_equal(filled, expected)
filled = s.resample('A', fill_method='ffill')
expected = stamps.resample('A', fill_method='ffill').to_period('A')
assert_series_equal(filled, expected)
def _get_new_index(self):
""" return our new index """
ax = self.ax
ax_attrs = ax._get_attributes_dict()
ax_attrs['freq'] = self.freq
obj = self._selected_obj
if len(ax) == 0:
new_index = PeriodIndex(data=[], **ax_attrs)
return obj.reindex(new_index)
start = ax[0].asfreq(self.freq, how=self.convention)
end = ax[-1].asfreq(self.freq, how='end')
return period_range(start, end, **ax_attrs)
def factorize(values, sort=False, order=None, na_sentinel=-1):
"""
Encode input values as an enumerated type or categorical variable
Parameters
----------
values : ndarray (1-d)
Sequence
sort : boolean, default False
Sort by values
order :
na_sentinel: int, default -1
Value to mark "not found"
Returns
-------
"""
from pandas.tseries.period import PeriodIndex
vals = np.asarray(values)
is_datetime = com.is_datetime64_dtype(vals)
(hash_klass, vec_klass), vals = _get_data_algo(vals, _hashtables)
table = hash_klass(len(vals))
uniques = vec_klass()
labels = table.get_labels(vals, uniques, 0, na_sentinel)
labels = com._ensure_platform_int(labels)
uniques = uniques.to_array()
if sort and len(uniques) > 0:
sorter = uniques.argsort()
reverse_indexer = np.empty(len(sorter), dtype=np.int_)
reverse_indexer.put(sorter, np.arange(len(sorter)))
mask = labels < 0
labels = reverse_indexer.take(labels)
np.putmask(labels, mask, -1)
uniques = uniques.take(sorter)
if is_datetime:
uniques = uniques.view('M8[ns]')
if isinstance(values, PeriodIndex):
uniques = PeriodIndex(ordinal=uniques, freq=values.freq)
return labels, uniques
def duplicated(values, keep='first'):
"""
Return boolean ndarray denoting duplicate values
.. versionadded:: 0.19.0
Parameters
----------
keep : {'first', 'last', False}, default 'first'
- ``first`` : Mark duplicates as ``True`` except for the first
occurrence.
- ``last`` : Mark duplicates as ``True`` except for the last
occurrence.
- False : Mark all duplicates as ``True``.
Returns
-------
duplicated : ndarray
"""
dtype = values.dtype
# no need to revert to original type
if is_datetime_or_timedelta_dtype(dtype) or is_datetimetz(dtype):
if isinstance(values, (ABCSeries, ABCIndex)):
values = values.values.view(np.int64)
else:
values = values.view(np.int64)
elif is_period_arraylike(values):
from pandas.tseries.period import PeriodIndex
values = PeriodIndex(values).asi8
elif is_categorical_dtype(dtype):
values = values.values.codes
elif isinstance(values, (ABCSeries, ABCIndex)):
values = values.values
if is_integer_dtype(dtype):
values = _ensure_int64(values)
duplicated = htable.duplicated_int64(values, keep=keep)
elif is_float_dtype(dtype):
values = _ensure_float64(values)
duplicated = htable.duplicated_float64(values, keep=keep)
else:
values = _ensure_object(values)
duplicated = htable.duplicated_object(values, keep=keep)
return duplicated
def _convert_1d(values, units, axis):
if not hasattr(axis, 'freq'):
raise TypeError('Axis must have `freq` set to convert to Periods')
valid_types = (compat.string_types, datetime, Period, pydt.date,
pydt.time)
if (isinstance(values, valid_types) or is_integer(values)
or is_float(values)):
return get_datevalue(values, axis.freq)
if isinstance(values, PeriodIndex):
return values.asfreq(axis.freq)._values
if isinstance(values, Index):
return values.map(lambda x: get_datevalue(x, axis.freq))
if is_period_arraylike(values):
return PeriodIndex(values, freq=axis.freq)._values
if isinstance(values, (list, tuple, np.ndarray, Index)):
return [get_datevalue(x, axis.freq) for x in values]
return values
def test_mixed_freq_regular_first(self):
import matplotlib.pyplot as plt
s1 = tm.makeTimeSeries()
s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]]
ax = s1.plot()
ax2 = s2.plot(style='g')
lines = ax2.get_lines()
idx1 = PeriodIndex(lines[0].get_xdata())
idx2 = PeriodIndex(lines[1].get_xdata())
self.assertTrue(idx1.equals(s1.index.to_period('B')))
self.assertTrue(idx2.equals(s2.index.to_period('B')))
left, right = ax2.get_xlim()
pidx = s1.index.to_period()
self.assertEqual(left, pidx[0].ordinal)
self.assertEqual(right, pidx[-1].ordinal)
def test_from_resampling_area_line_mixed(self):
idxh = date_range('1/1/1999', periods=52, freq='W')
idxl = date_range('1/1/1999', periods=12, freq='M')
high = DataFrame(np.random.rand(len(idxh), 3),
index=idxh,
columns=[0, 1, 2])
low = DataFrame(np.random.rand(len(idxl), 3),
index=idxl,
columns=[0, 1, 2])
# low to high
for kind1, kind2 in [('line', 'area'), ('area', 'line')]:
ax = low.plot(kind=kind1, stacked=True)
ax = high.plot(kind=kind2, stacked=True, ax=ax)
# check low dataframe result
expected_x = np.array([
1514, 1519, 1523, 1527, 1531, 1536, 1540, 1544, 1549, 1553,
1558, 1562
],
dtype=np.float64)
expected_y = np.zeros(len(expected_x), dtype=np.float64)
for i in range(3):
l = ax.lines[i]
self.assertEqual(PeriodIndex(l.get_xdata()).freq, idxh.freq)
self.assert_numpy_array_equal(l.get_xdata(orig=False),
expected_x)
# check stacked values are correct
expected_y += low[i].values
self.assert_numpy_array_equal(l.get_ydata(orig=False),
expected_y)
# check high dataframe result
expected_x = idxh.to_period().asi8.astype(np.float64)
expected_y = np.zeros(len(expected_x), dtype=np.float64)
for i in range(3):
l = ax.lines[3 + i]
self.assertEqual(
PeriodIndex(data=l.get_xdata()).freq, idxh.freq)
self.assert_numpy_array_equal(l.get_xdata(orig=False),
expected_x)
expected_y += high[i].values
self.assert_numpy_array_equal(l.get_ydata(orig=False),
expected_y)
# high to low
for kind1, kind2 in [('line', 'area'), ('area', 'line')]:
ax = high.plot(kind=kind1, stacked=True)
ax = low.plot(kind=kind2, stacked=True, ax=ax)
# check high dataframe result
expected_x = idxh.to_period().asi8.astype(np.float64)
expected_y = np.zeros(len(expected_x), dtype=np.float64)
for i in range(3):
l = ax.lines[i]
self.assertEqual(
PeriodIndex(data=l.get_xdata()).freq, idxh.freq)
self.assert_numpy_array_equal(l.get_xdata(orig=False),
expected_x)
expected_y += high[i].values
self.assert_numpy_array_equal(l.get_ydata(orig=False),
expected_y)
# check low dataframe result
expected_x = np.array([
1514, 1519, 1523, 1527, 1531, 1536, 1540, 1544, 1549, 1553,
1558, 1562
],
dtype=np.float64)
expected_y = np.zeros(len(expected_x), dtype=np.float64)
for i in range(3):
l = ax.lines[3 + i]
self.assertEqual(
PeriodIndex(data=l.get_xdata()).freq, idxh.freq)
self.assert_numpy_array_equal(l.get_xdata(orig=False),
expected_x)
expected_y += low[i].values
self.assert_numpy_array_equal(l.get_ydata(orig=False),
expected_y)