Python pandas._libs.lib.generate_bins_dt64() Examples
The following are 10
code examples of pandas._libs.lib.generate_bins_dt64().
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Example #1
| Source File: test_bin_groupby.py From recruit with Apache License 2.0 | 5 votes |
|
def test_generate_bins(self):
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6, 9], dtype=np.int64)
for func in [lib.generate_bins_dt64, generate_bins_generic]:
bins = func(values, binner, closed='left')
assert ((bins == np.array([2, 5, 6])).all())
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6, 6])).all())
for func in [lib.generate_bins_dt64, generate_bins_generic]:
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6], dtype=np.int64)
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6])).all())
msg = "Invalid length for values or for binner"
with pytest.raises(ValueError, match=msg):
generate_bins_generic(values, [], 'right')
with pytest.raises(ValueError, match=msg):
generate_bins_generic(values[:0], binner, 'right')
msg = "Values falls before first bin"
with pytest.raises(ValueError, match=msg):
generate_bins_generic(values, [4], 'right')
msg = "Values falls after last bin"
with pytest.raises(ValueError, match=msg):
generate_bins_generic(values, [-3, -1], 'right')
Example #2
| Source File: test_bin_groupby.py From vnpy_crypto with MIT License | 5 votes |
|
def test_generate_bins(self):
from pandas.core.groupby.groupby import generate_bins_generic
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6, 9], dtype=np.int64)
for func in [lib.generate_bins_dt64, generate_bins_generic]:
bins = func(values, binner, closed='left')
assert ((bins == np.array([2, 5, 6])).all())
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6, 6])).all())
for func in [lib.generate_bins_dt64, generate_bins_generic]:
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6], dtype=np.int64)
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6])).all())
pytest.raises(ValueError, generate_bins_generic, values, [],
'right')
pytest.raises(ValueError, generate_bins_generic, values[:0],
binner, 'right')
pytest.raises(ValueError, generate_bins_generic, values, [4],
'right')
pytest.raises(ValueError, generate_bins_generic, values, [-3, -1],
'right')
Example #3
| Source File: test_bin_groupby.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_generate_bins(self):
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6, 9], dtype=np.int64)
for func in [lib.generate_bins_dt64, generate_bins_generic]:
bins = func(values, binner, closed='left')
assert ((bins == np.array([2, 5, 6])).all())
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6, 6])).all())
for func in [lib.generate_bins_dt64, generate_bins_generic]:
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6], dtype=np.int64)
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6])).all())
msg = "Invalid length for values or for binner"
with pytest.raises(ValueError, match=msg):
generate_bins_generic(values, [], 'right')
with pytest.raises(ValueError, match=msg):
generate_bins_generic(values[:0], binner, 'right')
msg = "Values falls before first bin"
with pytest.raises(ValueError, match=msg):
generate_bins_generic(values, [4], 'right')
msg = "Values falls after last bin"
with pytest.raises(ValueError, match=msg):
generate_bins_generic(values, [-3, -1], 'right')
Example #4
| Source File: test_bin_groupby.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_generate_bins(self):
from pandas.core.groupby import generate_bins_generic
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6, 9], dtype=np.int64)
for func in [lib.generate_bins_dt64, generate_bins_generic]:
bins = func(values, binner, closed='left')
assert ((bins == np.array([2, 5, 6])).all())
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6, 6])).all())
for func in [lib.generate_bins_dt64, generate_bins_generic]:
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6], dtype=np.int64)
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6])).all())
pytest.raises(ValueError, generate_bins_generic, values, [],
'right')
pytest.raises(ValueError, generate_bins_generic, values[:0],
binner, 'right')
pytest.raises(ValueError, generate_bins_generic, values, [4],
'right')
pytest.raises(ValueError, generate_bins_generic, values, [-3, -1],
'right')
Example #5
| Source File: test_bin_groupby.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_generate_bins(self):
from pandas.core.groupby.groupby import generate_bins_generic
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6, 9], dtype=np.int64)
for func in [lib.generate_bins_dt64, generate_bins_generic]:
bins = func(values, binner, closed='left')
assert ((bins == np.array([2, 5, 6])).all())
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6, 6])).all())
for func in [lib.generate_bins_dt64, generate_bins_generic]:
values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
binner = np.array([0, 3, 6], dtype=np.int64)
bins = func(values, binner, closed='right')
assert ((bins == np.array([3, 6])).all())
pytest.raises(ValueError, generate_bins_generic, values, [],
'right')
pytest.raises(ValueError, generate_bins_generic, values[:0],
binner, 'right')
pytest.raises(ValueError, generate_bins_generic, values, [4],
'right')
pytest.raises(ValueError, generate_bins_generic, values, [-3, -1],
'right')
Example #6
| Source File: resample.py From recruit with Apache License 2.0 | 4 votes |
|
def _get_time_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 len(ax) == 0:
binner = labels = DatetimeIndex(
data=[], freq=self.freq, name=ax.name)
return binner, [], labels
first, last = _get_timestamp_range_edges(ax.min(), ax.max(),
self.freq,
closed=self.closed,
base=self.base)
# GH #12037
# use first/last directly instead of call replace() on them
# because replace() will swallow the nanosecond part
# thus last bin maybe slightly before the end if the end contains
# nanosecond part and lead to `Values falls after last bin` error
binner = labels = date_range(freq=self.freq,
start=first,
end=last,
tz=ax.tz,
name=ax.name,
ambiguous='infer',
nonexistent='shift_forward')
ax_values = ax.asi8
binner, bin_edges = self._adjust_bin_edges(binner, ax_values)
# general version, knowing nothing about relative frequencies
bins = lib.generate_bins_dt64(
ax_values, bin_edges, self.closed, hasnans=ax.hasnans)
if self.closed == 'right':
labels = binner
if self.label == 'right':
labels = labels[1:]
elif self.label == 'right':
labels = labels[1:]
if ax.hasnans:
binner = binner.insert(0, NaT)
labels = labels.insert(0, NaT)
# if we end up with more labels than bins
# adjust the labels
# GH4076
if len(bins) < len(labels):
labels = labels[:len(bins)]
return binner, bins, labels
Example #7
| Source File: resample.py From vnpy_crypto with MIT License | 4 votes |
|
def _get_period_bins(self, ax):
if not isinstance(ax, PeriodIndex):
raise TypeError('axis must be a PeriodIndex, but got '
'an instance of %r' % type(ax).__name__)
memb = ax.asfreq(self.freq, how=self.convention)
# NaT handling as in pandas._lib.lib.generate_bins_dt64()
nat_count = 0
if memb.hasnans:
nat_count = np.sum(memb._isnan)
memb = memb[~memb._isnan]
# if index contains no valid (non-NaT) values, return empty index
if not len(memb):
binner = labels = PeriodIndex(
data=[], freq=self.freq, name=ax.name)
return binner, [], labels
start = ax.min().asfreq(self.freq, how=self.convention)
end = ax.max().asfreq(self.freq, how='end')
labels = binner = PeriodIndex(start=start, end=end,
freq=self.freq, name=ax.name)
i8 = memb.asi8
freq_mult = self.freq.n
# when upsampling to subperiods, we need to generate enough bins
expected_bins_count = len(binner) * freq_mult
i8_extend = expected_bins_count - (i8[-1] - i8[0])
rng = np.arange(i8[0], i8[-1] + i8_extend, freq_mult)
rng += freq_mult
bins = memb.searchsorted(rng, side='left')
if nat_count > 0:
# NaT handling as in pandas._lib.lib.generate_bins_dt64()
# shift bins by the number of NaT
bins += nat_count
bins = np.insert(bins, 0, nat_count)
binner = binner.insert(0, tslib.NaT)
labels = labels.insert(0, tslib.NaT)
return binner, bins, labels
Example #8
| Source File: resample.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def _get_time_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 len(ax) == 0:
binner = labels = DatetimeIndex(
data=[], freq=self.freq, name=ax.name)
return binner, [], labels
first, last = _get_timestamp_range_edges(ax.min(), ax.max(),
self.freq,
closed=self.closed,
base=self.base)
# GH #12037
# use first/last directly instead of call replace() on them
# because replace() will swallow the nanosecond part
# thus last bin maybe slightly before the end if the end contains
# nanosecond part and lead to `Values falls after last bin` error
binner = labels = date_range(freq=self.freq,
start=first,
end=last,
tz=ax.tz,
name=ax.name,
ambiguous='infer',
nonexistent='shift_forward')
ax_values = ax.asi8
binner, bin_edges = self._adjust_bin_edges(binner, ax_values)
# general version, knowing nothing about relative frequencies
bins = lib.generate_bins_dt64(
ax_values, bin_edges, self.closed, hasnans=ax.hasnans)
if self.closed == 'right':
labels = binner
if self.label == 'right':
labels = labels[1:]
elif self.label == 'right':
labels = labels[1:]
if ax.hasnans:
binner = binner.insert(0, NaT)
labels = labels.insert(0, NaT)
# if we end up with more labels than bins
# adjust the labels
# GH4076
if len(bins) < len(labels):
labels = labels[:len(bins)]
return binner, bins, labels
Example #9
| Source File: resample.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def _get_period_bins(self, ax):
if not isinstance(ax, PeriodIndex):
raise TypeError('axis must be a PeriodIndex, but got '
'an instance of %r' % type(ax).__name__)
memb = ax.asfreq(self.freq, how=self.convention)
# NaT handling as in pandas._lib.lib.generate_bins_dt64()
nat_count = 0
if memb.hasnans:
nat_count = np.sum(memb._isnan)
memb = memb[~memb._isnan]
# if index contains no valid (non-NaT) values, return empty index
if not len(memb):
binner = labels = PeriodIndex(
data=[], freq=self.freq, name=ax.name)
return binner, [], labels
start = ax.min().asfreq(self.freq, how=self.convention)
end = ax.max().asfreq(self.freq, how='end')
labels = binner = PeriodIndex(start=start, end=end,
freq=self.freq, name=ax.name)
i8 = memb.asi8
freq_mult = self.freq.n
# when upsampling to subperiods, we need to generate enough bins
expected_bins_count = len(binner) * freq_mult
i8_extend = expected_bins_count - (i8[-1] - i8[0])
rng = np.arange(i8[0], i8[-1] + i8_extend, freq_mult)
rng += freq_mult
bins = memb.searchsorted(rng, side='left')
if nat_count > 0:
# NaT handling as in pandas._lib.lib.generate_bins_dt64()
# shift bins by the number of NaT
bins += nat_count
bins = np.insert(bins, 0, nat_count)
binner = binner.insert(0, tslib.NaT)
labels = labels.insert(0, tslib.NaT)
return binner, bins, labels
Example #10
| Source File: resample.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
def _get_period_bins(self, ax):
if not isinstance(ax, PeriodIndex):
raise TypeError('axis must be a PeriodIndex, but got '
'an instance of %r' % type(ax).__name__)
memb = ax.asfreq(self.freq, how=self.convention)
# NaT handling as in pandas._lib.lib.generate_bins_dt64()
nat_count = 0
if memb.hasnans:
nat_count = np.sum(memb._isnan)
memb = memb[~memb._isnan]
# if index contains no valid (non-NaT) values, return empty index
if not len(memb):
binner = labels = PeriodIndex(
data=[], freq=self.freq, name=ax.name)
return binner, [], labels
start = ax.min().asfreq(self.freq, how=self.convention)
end = ax.max().asfreq(self.freq, how='end')
labels = binner = PeriodIndex(start=start, end=end,
freq=self.freq, name=ax.name)
i8 = memb.asi8
freq_mult = self.freq.n
# when upsampling to subperiods, we need to generate enough bins
expected_bins_count = len(binner) * freq_mult
i8_extend = expected_bins_count - (i8[-1] - i8[0])
rng = np.arange(i8[0], i8[-1] + i8_extend, freq_mult)
rng += freq_mult
bins = memb.searchsorted(rng, side='left')
if nat_count > 0:
# NaT handling as in pandas._lib.lib.generate_bins_dt64()
# shift bins by the number of NaT
bins += nat_count
bins = np.insert(bins, 0, nat_count)
binner = binner.insert(0, tslib.NaT)
labels = labels.insert(0, tslib.NaT)
return binner, bins, labels
