Python pandas._libs.lib.item_from_zerodim() Examples
The following are 24
code examples of pandas._libs.lib.item_from_zerodim().
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Example #1
| Source File: sparse.py From recruit with Apache License 2.0 | 6 votes |
|
def _wrap_result(name, data, sparse_index, fill_value, dtype=None):
"""
wrap op result to have correct dtype
"""
if name.startswith('__'):
# e.g. __eq__ --> eq
name = name[2:-2]
if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):
dtype = np.bool
fill_value = lib.item_from_zerodim(fill_value)
if is_bool_dtype(dtype):
# fill_value may be np.bool_
fill_value = bool(fill_value)
return SparseArray(data,
sparse_index=sparse_index,
fill_value=fill_value,
dtype=dtype)
Example #2
| Source File: sparse.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def _wrap_result(name, data, sparse_index, fill_value, dtype=None):
"""
wrap op result to have correct dtype
"""
if name.startswith('__'):
# e.g. __eq__ --> eq
name = name[2:-2]
if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):
dtype = np.bool
fill_value = lib.item_from_zerodim(fill_value)
if is_bool_dtype(dtype):
# fill_value may be np.bool_
fill_value = bool(fill_value)
return SparseArray(data,
sparse_index=sparse_index,
fill_value=fill_value,
dtype=dtype)
Example #3
| Source File: test_inference.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_is_scalar_numpy_zerodim_arrays(self):
for zerodim in [np.array(1), np.array('foobar'),
np.array(np.datetime64('2014-01-01')),
np.array(np.timedelta64(1, 'h')),
np.array(np.datetime64('NaT'))]:
assert not is_scalar(zerodim)
assert is_scalar(lib.item_from_zerodim(zerodim))
Example #4
| Source File: test_inference.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_is_scalar_numpy_zerodim_arrays(self):
for zerodim in [np.array(1), np.array('foobar'),
np.array(np.datetime64('2014-01-01')),
np.array(np.timedelta64(1, 'h')),
np.array(np.datetime64('NaT'))]:
assert not is_scalar(zerodim)
assert is_scalar(lib.item_from_zerodim(zerodim))
Example #5
| Source File: test_inference.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_isscalar_numpy_zerodim_arrays(self):
for zerodim in [np.array(1), np.array('foobar'),
np.array(np.datetime64('2014-01-01')),
np.array(np.timedelta64(1, 'h')),
np.array(np.datetime64('NaT'))]:
assert not is_scalar(zerodim)
assert is_scalar(lib.item_from_zerodim(zerodim))
Example #6
| Source File: timedeltas.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def __rdivmod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
res1 = other // self
res2 = other - res1 * self
return res1, res2
# Note: TimedeltaIndex overrides this in call to cls._add_numeric_methods
Example #7
| Source File: timedeltas.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def __divmod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
res1 = self // other
res2 = self - res1 * other
return res1, res2
Example #8
| Source File: timedeltas.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def __rmod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
return other - (other // self) * self
Example #9
| Source File: timedeltas.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def __mod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
return self - (self // other) * other
Example #10
| Source File: timedeltas.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def __mul__(self, other):
other = lib.item_from_zerodim(other)
if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
return NotImplemented
if is_scalar(other):
# numpy will accept float and int, raise TypeError for others
result = self._data * other
freq = None
if self.freq is not None and not isna(other):
freq = self.freq * other
return type(self)(result, freq=freq)
if not hasattr(other, "dtype"):
# list, tuple
other = np.array(other)
if len(other) != len(self) and not is_timedelta64_dtype(other):
# Exclude timedelta64 here so we correctly raise TypeError
# for that instead of ValueError
raise ValueError("Cannot multiply with unequal lengths")
if is_object_dtype(other):
# this multiplication will succeed only if all elements of other
# are int or float scalars, so we will end up with
# timedelta64[ns]-dtyped result
result = [self[n] * other[n] for n in range(len(self))]
result = np.array(result)
return type(self)(result)
# numpy will accept float or int dtype, raise TypeError for others
result = self._data * other
return type(self)(result)
Example #11
| Source File: test_inference.py From recruit with Apache License 2.0 | 5 votes |
|
def test_is_scalar_numpy_zerodim_arrays(self):
for zerodim in [np.array(1), np.array('foobar'),
np.array(np.datetime64('2014-01-01')),
np.array(np.timedelta64(1, 'h')),
np.array(np.datetime64('NaT'))]:
assert not is_scalar(zerodim)
assert is_scalar(lib.item_from_zerodim(zerodim))
Example #12
| Source File: datetimelike.py From vnpy_crypto with MIT License | 5 votes |
|
def _evaluate_compare(self, other, op):
"""
We have been called because a comparison between
8 aware arrays. numpy >= 1.11 will
now warn about NaT comparisons
"""
# coerce to a similar object
if not isinstance(other, type(self)):
if not is_list_like(other):
# scalar
other = [other]
elif is_scalar(lib.item_from_zerodim(other)):
# ndarray scalar
other = [other.item()]
other = type(self)(other)
# compare
result = op(self.asi8, other.asi8)
# technically we could support bool dtyped Index
# for now just return the indexing array directly
mask = (self._isnan) | (other._isnan)
if is_bool_dtype(result):
result[mask] = False
return result
result[mask] = iNaT
try:
return Index(result)
except TypeError:
return result
Example #13
| Source File: test_inference.py From vnpy_crypto with MIT License | 5 votes |
|
def test_is_scalar_numpy_zerodim_arrays(self):
for zerodim in [np.array(1), np.array('foobar'),
np.array(np.datetime64('2014-01-01')),
np.array(np.timedelta64(1, 'h')),
np.array(np.datetime64('NaT'))]:
assert not is_scalar(zerodim)
assert is_scalar(lib.item_from_zerodim(zerodim))
Example #14
| Source File: timedeltas.py From recruit with Apache License 2.0 | 5 votes |
|
def __rdivmod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
res1 = other // self
res2 = other - res1 * self
return res1, res2
# Note: TimedeltaIndex overrides this in call to cls._add_numeric_methods
Example #15
| Source File: timedeltas.py From recruit with Apache License 2.0 | 5 votes |
|
def __divmod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
res1 = self // other
res2 = self - res1 * other
return res1, res2
Example #16
| Source File: timedeltas.py From recruit with Apache License 2.0 | 5 votes |
|
def __rmod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
return other - (other // self) * self
Example #17
| Source File: timedeltas.py From recruit with Apache License 2.0 | 5 votes |
|
def __mod__(self, other):
# Note: This is a naive implementation, can likely be optimized
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
return self - (self // other) * other
Example #18
| Source File: timedeltas.py From recruit with Apache License 2.0 | 5 votes |
|
def __mul__(self, other):
other = lib.item_from_zerodim(other)
if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
return NotImplemented
if is_scalar(other):
# numpy will accept float and int, raise TypeError for others
result = self._data * other
freq = None
if self.freq is not None and not isna(other):
freq = self.freq * other
return type(self)(result, freq=freq)
if not hasattr(other, "dtype"):
# list, tuple
other = np.array(other)
if len(other) != len(self) and not is_timedelta64_dtype(other):
# Exclude timedelta64 here so we correctly raise TypeError
# for that instead of ValueError
raise ValueError("Cannot multiply with unequal lengths")
if is_object_dtype(other):
# this multiplication will succeed only if all elements of other
# are int or float scalars, so we will end up with
# timedelta64[ns]-dtyped result
result = [self[n] * other[n] for n in range(len(self))]
result = np.array(result)
return type(self)(result)
# numpy will accept float or int dtype, raise TypeError for others
result = self._data * other
return type(self)(result)
Example #19
| Source File: datetimelike.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def __add__(self, other):
other = lib.item_from_zerodim(other)
if isinstance(other, (ABCSeries, ABCDataFrame)):
return NotImplemented
# scalar others
elif other is NaT:
result = self._add_nat()
elif isinstance(other, (Tick, timedelta, np.timedelta64)):
result = self._add_delta(other)
elif isinstance(other, DateOffset):
# specifically _not_ a Tick
result = self._add_offset(other)
elif isinstance(other, (datetime, np.datetime64)):
result = self._add_datetimelike_scalar(other)
elif lib.is_integer(other):
# This check must come after the check for np.timedelta64
# as is_integer returns True for these
if not is_period_dtype(self):
maybe_integer_op_deprecated(self)
result = self._time_shift(other)
# array-like others
elif is_timedelta64_dtype(other):
# TimedeltaIndex, ndarray[timedelta64]
result = self._add_delta(other)
elif is_offsetlike(other):
# Array/Index of DateOffset objects
result = self._addsub_offset_array(other, operator.add)
elif is_datetime64_dtype(other) or is_datetime64tz_dtype(other):
# DatetimeIndex, ndarray[datetime64]
return self._add_datetime_arraylike(other)
elif is_integer_dtype(other):
if not is_period_dtype(self):
maybe_integer_op_deprecated(self)
result = self._addsub_int_array(other, operator.add)
elif is_float_dtype(other):
# Explicitly catch invalid dtypes
raise TypeError("cannot add {dtype}-dtype to {cls}"
.format(dtype=other.dtype,
cls=type(self).__name__))
elif is_period_dtype(other):
# if self is a TimedeltaArray and other is a PeriodArray with
# a timedelta-like (i.e. Tick) freq, this operation is valid.
# Defer to the PeriodArray implementation.
# In remaining cases, this will end up raising TypeError.
return NotImplemented
elif is_extension_array_dtype(other):
# Categorical op will raise; defer explicitly
return NotImplemented
else: # pragma: no cover
return NotImplemented
if is_timedelta64_dtype(result) and isinstance(result, np.ndarray):
from pandas.core.arrays import TimedeltaArray
# TODO: infer freq?
return TimedeltaArray(result)
return result
Example #20
| Source File: timedeltas.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def __truediv__(self, other):
# timedelta / X is well-defined for timedelta-like or numeric X
other = lib.item_from_zerodim(other)
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
if other is NaT:
# specifically timedelta64-NaT
result = np.empty(self.shape, dtype=np.float64)
result.fill(np.nan)
return result
# otherwise, dispatch to Timedelta implementation
return self._data / other
elif lib.is_scalar(other):
# assume it is numeric
result = self._data / other
freq = None
if self.freq is not None:
# Tick division is not implemented, so operate on Timedelta
freq = self.freq.delta / other
return type(self)(result, freq=freq)
if not hasattr(other, "dtype"):
# e.g. list, tuple
other = np.array(other)
if len(other) != len(self):
raise ValueError("Cannot divide vectors with unequal lengths")
elif is_timedelta64_dtype(other):
# let numpy handle it
return self._data / other
elif is_object_dtype(other):
# Note: we do not do type inference on the result, so either
# an object array or numeric-dtyped (if numpy does inference)
# will be returned. GH#23829
result = [self[n] / other[n] for n in range(len(self))]
result = np.array(result)
return result
else:
result = self._data / other
return type(self)(result)
Example #21
| Source File: timedeltas.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def __rfloordiv__(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if is_scalar(other):
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
if other is NaT:
# treat this specifically as timedelta-NaT
result = np.empty(self.shape, dtype=np.float64)
result.fill(np.nan)
return result
# dispatch to Timedelta implementation
result = other.__floordiv__(self._data)
return result
raise TypeError("Cannot divide {typ} by {cls}"
.format(typ=type(other).__name__,
cls=type(self).__name__))
if not hasattr(other, "dtype"):
# list, tuple
other = np.array(other)
if len(other) != len(self):
raise ValueError("Cannot divide with unequal lengths")
elif is_timedelta64_dtype(other):
other = type(self)(other)
# numpy timedelta64 does not natively support floordiv, so operate
# on the i8 values
result = other.asi8 // self.asi8
mask = self._isnan | other._isnan
if mask.any():
result = result.astype(np.int64)
result[mask] = np.nan
return result
elif is_object_dtype(other):
result = [other[n] // self[n] for n in range(len(self))]
result = np.array(result)
return result
else:
dtype = getattr(other, "dtype", type(other).__name__)
raise TypeError("Cannot divide {typ} by {cls}"
.format(typ=dtype, cls=type(self).__name__))
Example #22
| Source File: timedeltas.py From recruit with Apache License 2.0 | 4 votes |
|
def __rfloordiv__(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
other = lib.item_from_zerodim(other)
if is_scalar(other):
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
if other is NaT:
# treat this specifically as timedelta-NaT
result = np.empty(self.shape, dtype=np.float64)
result.fill(np.nan)
return result
# dispatch to Timedelta implementation
result = other.__floordiv__(self._data)
return result
raise TypeError("Cannot divide {typ} by {cls}"
.format(typ=type(other).__name__,
cls=type(self).__name__))
if not hasattr(other, "dtype"):
# list, tuple
other = np.array(other)
if len(other) != len(self):
raise ValueError("Cannot divide with unequal lengths")
elif is_timedelta64_dtype(other):
other = type(self)(other)
# numpy timedelta64 does not natively support floordiv, so operate
# on the i8 values
result = other.asi8 // self.asi8
mask = self._isnan | other._isnan
if mask.any():
result = result.astype(np.int64)
result[mask] = np.nan
return result
elif is_object_dtype(other):
result = [other[n] // self[n] for n in range(len(self))]
result = np.array(result)
return result
else:
dtype = getattr(other, "dtype", type(other).__name__)
raise TypeError("Cannot divide {typ} by {cls}"
.format(typ=dtype, cls=type(self).__name__))
Example #23
| Source File: timedeltas.py From recruit with Apache License 2.0 | 4 votes |
|
def __truediv__(self, other):
# timedelta / X is well-defined for timedelta-like or numeric X
other = lib.item_from_zerodim(other)
if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
return NotImplemented
if isinstance(other, (timedelta, np.timedelta64, Tick)):
other = Timedelta(other)
if other is NaT:
# specifically timedelta64-NaT
result = np.empty(self.shape, dtype=np.float64)
result.fill(np.nan)
return result
# otherwise, dispatch to Timedelta implementation
return self._data / other
elif lib.is_scalar(other):
# assume it is numeric
result = self._data / other
freq = None
if self.freq is not None:
# Tick division is not implemented, so operate on Timedelta
freq = self.freq.delta / other
return type(self)(result, freq=freq)
if not hasattr(other, "dtype"):
# e.g. list, tuple
other = np.array(other)
if len(other) != len(self):
raise ValueError("Cannot divide vectors with unequal lengths")
elif is_timedelta64_dtype(other):
# let numpy handle it
return self._data / other
elif is_object_dtype(other):
# Note: we do not do type inference on the result, so either
# an object array or numeric-dtyped (if numpy does inference)
# will be returned. GH#23829
result = [self[n] / other[n] for n in range(len(self))]
result = np.array(result)
return result
else:
result = self._data / other
return type(self)(result)
Example #24
| Source File: datetimelike.py From recruit with Apache License 2.0 | 4 votes |
|
def __add__(self, other):
other = lib.item_from_zerodim(other)
if isinstance(other, (ABCSeries, ABCDataFrame)):
return NotImplemented
# scalar others
elif other is NaT:
result = self._add_nat()
elif isinstance(other, (Tick, timedelta, np.timedelta64)):
result = self._add_delta(other)
elif isinstance(other, DateOffset):
# specifically _not_ a Tick
result = self._add_offset(other)
elif isinstance(other, (datetime, np.datetime64)):
result = self._add_datetimelike_scalar(other)
elif lib.is_integer(other):
# This check must come after the check for np.timedelta64
# as is_integer returns True for these
if not is_period_dtype(self):
maybe_integer_op_deprecated(self)
result = self._time_shift(other)
# array-like others
elif is_timedelta64_dtype(other):
# TimedeltaIndex, ndarray[timedelta64]
result = self._add_delta(other)
elif is_offsetlike(other):
# Array/Index of DateOffset objects
result = self._addsub_offset_array(other, operator.add)
elif is_datetime64_dtype(other) or is_datetime64tz_dtype(other):
# DatetimeIndex, ndarray[datetime64]
return self._add_datetime_arraylike(other)
elif is_integer_dtype(other):
if not is_period_dtype(self):
maybe_integer_op_deprecated(self)
result = self._addsub_int_array(other, operator.add)
elif is_float_dtype(other):
# Explicitly catch invalid dtypes
raise TypeError("cannot add {dtype}-dtype to {cls}"
.format(dtype=other.dtype,
cls=type(self).__name__))
elif is_period_dtype(other):
# if self is a TimedeltaArray and other is a PeriodArray with
# a timedelta-like (i.e. Tick) freq, this operation is valid.
# Defer to the PeriodArray implementation.
# In remaining cases, this will end up raising TypeError.
return NotImplemented
elif is_extension_array_dtype(other):
# Categorical op will raise; defer explicitly
return NotImplemented
else: # pragma: no cover
return NotImplemented
if is_timedelta64_dtype(result) and isinstance(result, np.ndarray):
from pandas.core.arrays import TimedeltaArray
# TODO: infer freq?
return TimedeltaArray(result)
return result
