def __new__(cls, data, closed=None,
name=None, copy=False, dtype=None,
fastpath=False, verify_integrity=True):
if fastpath:
return cls._simple_new(data.left, data.right, closed, name,
copy=copy, verify_integrity=False)
if name is None and hasattr(data, 'name'):
name = data.name
if isinstance(data, IntervalIndex):
left = data.left
right = data.right
closed = data.closed
else:
# don't allow scalars
if is_scalar(data):
cls._scalar_data_error(data)
data = maybe_convert_platform(data)
left, right, infer_closed = intervals_to_interval_bounds(data)
if _all_not_none(closed, infer_closed) and closed != infer_closed:
# GH 18421
msg = ("conflicting values for closed: constructor got "
"'{closed}', inferred from data '{infer_closed}'"
.format(closed=closed, infer_closed=infer_closed))
raise ValueError(msg)
closed = closed or infer_closed
return cls._simple_new(left, right, closed, name,
copy=copy, verify_integrity=verify_integrity)
def __new__(cls, data, closed=None, dtype=None, copy=False,
verify_integrity=True):
if isinstance(data, ABCSeries) and is_interval_dtype(data):
data = data.values
if isinstance(data, (cls, ABCIntervalIndex)):
left = data.left
right = data.right
closed = closed or data.closed
else:
# don't allow scalars
if is_scalar(data):
msg = ("{}(...) must be called with a collection of some kind,"
" {} was passed")
raise TypeError(msg.format(cls.__name__, data))
# might need to convert empty or purely na data
data = maybe_convert_platform_interval(data)
left, right, infer_closed = intervals_to_interval_bounds(
data, validate_closed=closed is None)
closed = closed or infer_closed
return cls._simple_new(left, right, closed, copy=copy, dtype=dtype,
verify_integrity=verify_integrity)
def from_intervals(cls, data, name=None, copy=False):
"""
Construct an IntervalIndex from a 1d array of Interval objects
Parameters
----------
data : array-like (1-dimensional)
Array of Interval objects. All intervals must be closed on the same
sides.
name : object, optional
Name to be stored in the index.
copy : boolean, default False
by-default copy the data, this is compat only and ignored
Examples
--------
>>> IntervalIndex.from_intervals([Interval(0, 1), Interval(1, 2)])
IntervalIndex(left=[0, 1],
right=[1, 2],
closed='right')
The generic Index constructor work identically when it infers an array
of all intervals:
>>> Index([Interval(0, 1), Interval(1, 2)])
IntervalIndex(left=[0, 1],
right=[1, 2],
closed='right')
"""
data = np.asarray(data)
left, right, closed = intervals_to_interval_bounds(data)
return cls.from_arrays(left, right, closed, name=name, copy=False)
def from_intervals(cls, data, name=None, copy=False):
"""
Construct an IntervalIndex from a 1d array of Interval objects
Parameters
----------
data : array-like (1-dimensional)
Array of Interval objects. All intervals must be closed on the same
sides.
name : object, optional
Name to be stored in the index.
copy : boolean, default False
by-default copy the data, this is compat only and ignored
Examples
--------
>>> IntervalIndex.from_intervals([Interval(0, 1), Interval(1, 2)])
IntervalIndex(left=[0, 1],
right=[1, 2],
closed='right')
The generic Index constructor work identically when it infers an array
of all intervals:
>>> Index([Interval(0, 1), Interval(1, 2)])
IntervalIndex(left=[0, 1],
right=[1, 2],
closed='right')
"""
data = np.asarray(data)
left, right, closed = intervals_to_interval_bounds(data)
return cls.from_arrays(left, right, closed, name=name, copy=False)
def __new__(cls, data, closed=None,
name=None, copy=False, dtype=None,
fastpath=False, verify_integrity=True):
if fastpath:
return cls._simple_new(data.left, data.right, closed, name,
copy=copy, verify_integrity=False)
if name is None and hasattr(data, 'name'):
name = data.name
if isinstance(data, IntervalIndex):
left = data.left
right = data.right
closed = data.closed
else:
# don't allow scalars
if is_scalar(data):
cls._scalar_data_error(data)
data = maybe_convert_platform(data)
left, right, infer_closed = intervals_to_interval_bounds(data)
if _all_not_none(closed, infer_closed) and closed != infer_closed:
# GH 18421
msg = ("conflicting values for closed: constructor got "
"'{closed}', inferred from data '{infer_closed}'"
.format(closed=closed, infer_closed=infer_closed))
raise ValueError(msg)
closed = closed or infer_closed
return cls._simple_new(left, right, closed, name,
copy=copy, verify_integrity=verify_integrity)
def __new__(
cls,
data,
closed=None,
dtype=None,
copy: bool = False,
verify_integrity: bool = True,
):
if isinstance(data, (ABCSeries, ABCIntervalIndex)) and is_interval_dtype(
data.dtype
):
data = data._values # TODO: extract_array?
if isinstance(data, cls):
left = data._left
right = data._right
closed = closed or data.closed
if dtype is None or data.dtype == dtype:
# This path will preserve id(result._combined)
# TODO: could also validate dtype before going to simple_new
combined = data._combined
if copy:
combined = combined.copy()
result = cls._simple_new(combined, closed=closed)
if verify_integrity:
result._validate()
return result
else:
# don't allow scalars
if is_scalar(data):
msg = (
f"{cls.__name__}(...) must be called with a collection "
f"of some kind, {data} was passed"
)
raise TypeError(msg)
# might need to convert empty or purely na data
data = maybe_convert_platform_interval(data)
left, right, infer_closed = intervals_to_interval_bounds(
data, validate_closed=closed is None
)
closed = closed or infer_closed
closed = closed or "right"
left, right = _maybe_cast_inputs(left, right, copy, dtype)
combined = _get_combined_data(left, right)
result = cls._simple_new(combined, closed=closed)
if verify_integrity:
result._validate()
return result
def from_intervals(cls, data, name=None, copy=False):
"""
Construct an IntervalIndex from a 1d array of Interval objects
Parameters
----------
data : array-like (1-dimensional)
Array of Interval objects. All intervals must be closed on the same
sides.
name : object, optional
Name to be stored in the index.
copy : boolean, default False
by-default copy the data, this is compat only and ignored
Examples
--------
>>> pd.IntervalIndex.from_intervals([pd.Interval(0, 1),
... pd.Interval(1, 2)])
IntervalIndex([(0, 1], (1, 2]]
closed='right', dtype='interval[int64]')
The generic Index constructor work identically when it infers an array
of all intervals:
>>> pd.Index([pd.Interval(0, 1), pd.Interval(1, 2)])
IntervalIndex([(0, 1], (1, 2]]
closed='right', dtype='interval[int64]')
See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex
IntervalIndex.from_arrays : Construct an IntervalIndex from a left and
right array
IntervalIndex.from_breaks : Construct an IntervalIndex from an array of
splits
IntervalIndex.from_tuples : Construct an IntervalIndex from a
list/array of tuples
"""
if isinstance(data, IntervalIndex):
left, right, closed = data.left, data.right, data.closed
name = name or data.name
else:
data = maybe_convert_platform(data)
left, right, closed = intervals_to_interval_bounds(data)
return cls.from_arrays(left, right, closed, name=name, copy=False)
def from_intervals(cls, data, name=None, copy=False):
"""
Construct an IntervalIndex from a 1d array of Interval objects
Parameters
----------
data : array-like (1-dimensional)
Array of Interval objects. All intervals must be closed on the same
sides.
name : object, optional
Name to be stored in the index.
copy : boolean, default False
by-default copy the data, this is compat only and ignored
Examples
--------
>>> pd.IntervalIndex.from_intervals([pd.Interval(0, 1),
... pd.Interval(1, 2)])
IntervalIndex([(0, 1], (1, 2]]
closed='right', dtype='interval[int64]')
The generic Index constructor work identically when it infers an array
of all intervals:
>>> pd.Index([pd.Interval(0, 1), pd.Interval(1, 2)])
IntervalIndex([(0, 1], (1, 2]]
closed='right', dtype='interval[int64]')
See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex
IntervalIndex.from_arrays : Construct an IntervalIndex from a left and
right array
IntervalIndex.from_breaks : Construct an IntervalIndex from an array of
splits
IntervalIndex.from_tuples : Construct an IntervalIndex from a
list/array of tuples
"""
if isinstance(data, IntervalIndex):
left, right, closed = data.left, data.right, data.closed
name = name or data.name
else:
data = maybe_convert_platform(data)
left, right, closed = intervals_to_interval_bounds(data)
return cls.from_arrays(left, right, closed, name=name, copy=False)
def __new__(
cls,
data,
closed=None,
dtype=None,
copy: bool = False,
verify_integrity: bool = True,
):
if isinstance(data, (ABCSeries, ABCIntervalIndex)) and is_interval_dtype(
data.dtype
):
data = data._values # TODO: extract_array?
if isinstance(data, cls):
left = data._left
right = data._right
closed = closed or data.closed
else:
# don't allow scalars
if is_scalar(data):
msg = (
f"{cls.__name__}(...) must be called with a collection "
f"of some kind, {data} was passed"
)
raise TypeError(msg)
# might need to convert empty or purely na data
data = maybe_convert_platform_interval(data)
left, right, infer_closed = intervals_to_interval_bounds(
data, validate_closed=closed is None
)
closed = closed or infer_closed
return cls._simple_new(
left,
right,
closed,
copy=copy,
dtype=dtype,
verify_integrity=verify_integrity,
)
