Python pandas._libs.lib.is_integer() Examples
The following are 11
code examples of pandas._libs.lib.is_integer().
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Example #1
| Source File: datetimelike.py From recruit with Apache License 2.0 | 6 votes |
|
def validate_periods(periods):
"""
If a `periods` argument is passed to the Datetime/Timedelta Array/Index
constructor, cast it to an integer.
Parameters
----------
periods : None, float, int
Returns
-------
periods : None or int
Raises
------
TypeError
if periods is None, float, or int
"""
if periods is not None:
if lib.is_float(periods):
periods = int(periods)
elif not lib.is_integer(periods):
raise TypeError('periods must be a number, got {periods}'
.format(periods=periods))
return periods
Example #2
| Source File: range.py From recruit with Apache License 2.0 | 6 votes |
|
def __floordiv__(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame)):
return NotImplemented
if is_integer(other) and other != 0:
if (len(self) == 0 or
self._start % other == 0 and
self._step % other == 0):
start = self._start // other
step = self._step // other
stop = start + len(self) * step
return RangeIndex._simple_new(
start, stop, step, name=self.name)
if len(self) == 1:
start = self._start // other
return RangeIndex._simple_new(
start, start + 1, 1, name=self.name)
return self._int64index // other
Example #3
| Source File: datetimelike.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def validate_periods(periods):
"""
If a `periods` argument is passed to the Datetime/Timedelta Array/Index
constructor, cast it to an integer.
Parameters
----------
periods : None, float, int
Returns
-------
periods : None or int
Raises
------
TypeError
if periods is None, float, or int
"""
if periods is not None:
if lib.is_float(periods):
periods = int(periods)
elif not lib.is_integer(periods):
raise TypeError('periods must be a number, got {periods}'
.format(periods=periods))
return periods
Example #4
| Source File: range.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def __floordiv__(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame)):
return NotImplemented
if is_integer(other) and other != 0:
if (len(self) == 0 or
self._start % other == 0 and
self._step % other == 0):
start = self._start // other
step = self._step // other
stop = start + len(self) * step
return RangeIndex._simple_new(
start, stop, step, name=self.name)
if len(self) == 1:
start = self._start // other
return RangeIndex._simple_new(
start, start + 1, 1, name=self.name)
return self._int64index // other
Example #5
| Source File: range.py From recruit with Apache License 2.0 | 5 votes |
|
def _simple_new(cls, start, stop=None, step=None, name=None,
dtype=None, **kwargs):
result = object.__new__(cls)
# handle passed None, non-integers
if start is None and stop is None:
# empty
start, stop, step = 0, 0, 1
if start is None or not is_integer(start):
try:
return RangeIndex(start, stop, step, name=name, **kwargs)
except TypeError:
return Index(start, stop, step, name=name, **kwargs)
result._start = start
result._stop = stop or 0
result._step = step or 1
result.name = name
for k, v in compat.iteritems(kwargs):
setattr(result, k, v)
result._reset_identity()
return result
# --------------------------------------------------------------------
Example #6
| Source File: inference.py From vnpy_crypto with MIT License | 5 votes |
|
def is_number(obj):
"""
Check if the object is a number.
Returns True when the object is a number, and False if is not.
Parameters
----------
obj : any type
The object to check if is a number.
Returns
-------
is_number : bool
Whether `obj` is a number or not.
See Also
--------
pandas.api.types.is_integer: checks a subgroup of numbers
Examples
--------
>>> pd.api.types.is_number(1)
True
>>> pd.api.types.is_number(7.15)
True
Booleans are valid because they are int subclass.
>>> pd.api.types.is_number(False)
True
>>> pd.api.types.is_number("foo")
False
>>> pd.api.types.is_number("5")
False
"""
return isinstance(obj, (Number, np.number))
Example #7
| Source File: range.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def _simple_new(cls, start, stop=None, step=None, name=None,
dtype=None, **kwargs):
result = object.__new__(cls)
# handle passed None, non-integers
if start is None and stop is None:
# empty
start, stop, step = 0, 0, 1
if start is None or not is_integer(start):
try:
return RangeIndex(start, stop, step, name=name, **kwargs)
except TypeError:
return Index(start, stop, step, name=name, **kwargs)
result._start = start
result._stop = stop or 0
result._step = step or 1
result.name = name
for k, v in compat.iteritems(kwargs):
setattr(result, k, v)
result._reset_identity()
return result
# --------------------------------------------------------------------
Example #8
| Source File: datetimelike.py From recruit with Apache License 2.0 | 4 votes |
|
def __getitem__(self, key):
"""
This getitem defers to the underlying array, which by-definition can
only handle list-likes, slices, and integer scalars
"""
is_int = lib.is_integer(key)
if lib.is_scalar(key) and not is_int:
raise IndexError("only integers, slices (`:`), ellipsis (`...`), "
"numpy.newaxis (`None`) and integer or boolean "
"arrays are valid indices")
getitem = self._data.__getitem__
if is_int:
val = getitem(key)
return self._box_func(val)
if com.is_bool_indexer(key):
key = np.asarray(key, dtype=bool)
if key.all():
key = slice(0, None, None)
else:
key = lib.maybe_booleans_to_slice(key.view(np.uint8))
is_period = is_period_dtype(self)
if is_period:
freq = self.freq
else:
freq = None
if isinstance(key, slice):
if self.freq is not None and key.step is not None:
freq = key.step * self.freq
else:
freq = self.freq
elif key is Ellipsis:
# GH#21282 indexing with Ellipsis is similar to a full slice,
# should preserve `freq` attribute
freq = self.freq
result = getitem(key)
if result.ndim > 1:
# To support MPL which performs slicing with 2 dim
# even though it only has 1 dim by definition
if is_period:
return self._simple_new(result, dtype=self.dtype, freq=freq)
return result
return self._simple_new(result, dtype=self.dtype, freq=freq)
Example #9
| 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
Example #10
| Source File: datetimelike.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def __getitem__(self, key):
"""
This getitem defers to the underlying array, which by-definition can
only handle list-likes, slices, and integer scalars
"""
is_int = lib.is_integer(key)
if lib.is_scalar(key) and not is_int:
raise IndexError("only integers, slices (`:`), ellipsis (`...`), "
"numpy.newaxis (`None`) and integer or boolean "
"arrays are valid indices")
getitem = self._data.__getitem__
if is_int:
val = getitem(key)
return self._box_func(val)
if com.is_bool_indexer(key):
key = np.asarray(key, dtype=bool)
if key.all():
key = slice(0, None, None)
else:
key = lib.maybe_booleans_to_slice(key.view(np.uint8))
is_period = is_period_dtype(self)
if is_period:
freq = self.freq
else:
freq = None
if isinstance(key, slice):
if self.freq is not None and key.step is not None:
freq = key.step * self.freq
else:
freq = self.freq
elif key is Ellipsis:
# GH#21282 indexing with Ellipsis is similar to a full slice,
# should preserve `freq` attribute
freq = self.freq
result = getitem(key)
if result.ndim > 1:
# To support MPL which performs slicing with 2 dim
# even though it only has 1 dim by definition
if is_period:
return self._simple_new(result, dtype=self.dtype, freq=freq)
return result
return self._simple_new(result, dtype=self.dtype, freq=freq)
Example #11
| 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
