def __new__(
cls,
data,
inclusive=None,
closed: None | lib.NoDefault = lib.no_default,
dtype: Dtype | None = None,
copy: bool = False,
name: Hashable = None,
verify_integrity: bool = True,
) -> IntervalIndex:
inclusive, closed = _warning_interval(inclusive, closed)
name = maybe_extract_name(name, data, cls)
with rewrite_exception("IntervalArray", cls.__name__):
array = IntervalArray(
data,
inclusive=inclusive,
copy=copy,
dtype=dtype,
verify_integrity=verify_integrity,
)
return cls._simple_new(array, name)
def __init__(
self,
subtype,
inclusive: str | None = None,
closed: None | lib.NoDefault = lib.no_default,
) -> None:
# attributes need to be set first before calling
# super init (as that calls serialize)
inclusive, closed = _warning_interval(inclusive, closed)
assert inclusive in VALID_CLOSED
self._closed = inclusive
if not isinstance(subtype, pyarrow.DataType):
subtype = pyarrow.type_for_alias(str(subtype))
self._subtype = subtype
storage_type = pyarrow.struct([("left", subtype), ("right", subtype)])
pyarrow.ExtensionType.__init__(self, storage_type, "pandas.interval")
def from_tuples(
cls,
data,
inclusive=None,
closed: None | lib.NoDefault = lib.no_default,
name: Hashable = None,
copy: bool = False,
dtype: Dtype | None = None,
) -> IntervalIndex:
inclusive, closed = _warning_interval(inclusive, closed)
if inclusive is None:
inclusive = "both"
with rewrite_exception("IntervalArray", cls.__name__):
arr = IntervalArray.from_tuples(
data, inclusive=inclusive, copy=copy, dtype=dtype
)
return cls._simple_new(arr, name=name)
def interval_range(
start=None,
end=None,
periods=None,
freq=None,
name: Hashable = None,
closed: IntervalClosedType | lib.NoDefault = lib.no_default,
inclusive: IntervalClosedType | None = None,
) -> IntervalIndex:
"""
Return a fixed frequency IntervalIndex.
Parameters
----------
start : numeric or datetime-like, default None
Left bound for generating intervals.
end : numeric or datetime-like, default None
Right bound for generating intervals.
periods : int, default None
Number of periods to generate.
freq : numeric, str, or DateOffset, default None
The length of each interval. Must be consistent with the type of start
and end, e.g. 2 for numeric, or '5H' for datetime-like. Default is 1
for numeric and 'D' for datetime-like.
name : str, default None
Name of the resulting IntervalIndex.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both
or neither.
.. deprecated:: 1.5.0
Argument `closed` has been deprecated to standardize boundary inputs.
Use `inclusive` instead, to set each bound as closed or open.
inclusive : {"both", "neither", "left", "right"}, default "both"
Include boundaries; Whether to set each bound as closed or open.
.. versionadded:: 1.5.0
Returns
-------
IntervalIndex
See Also
--------
IntervalIndex : An Index of intervals that are all closed on the same side.
Notes
-----
Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
exactly three must be specified. If ``freq`` is omitted, the resulting
``IntervalIndex`` will have ``periods`` linearly spaced elements between
``start`` and ``end``, inclusively.
To learn more about datetime-like frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Numeric ``start`` and ``end`` is supported.
>>> pd.interval_range(start=0, end=5, inclusive="right")
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
dtype='interval[int64, right]')
Additionally, datetime-like input is also supported.
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
... end=pd.Timestamp('2017-01-04'), inclusive="right")
IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03],
(2017-01-03, 2017-01-04]],
dtype='interval[datetime64[ns], right]')
The ``freq`` parameter specifies the frequency between the left and right.
endpoints of the individual intervals within the ``IntervalIndex``. For
numeric ``start`` and ``end``, the frequency must also be numeric.
>>> pd.interval_range(start=0, periods=4, freq=1.5, inclusive="right")
IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
dtype='interval[float64, right]')
Similarly, for datetime-like ``start`` and ``end``, the frequency must be
convertible to a DateOffset.
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
... periods=3, freq='MS', inclusive="right")
IntervalIndex([(2017-01-01, 2017-02-01], (2017-02-01, 2017-03-01],
(2017-03-01, 2017-04-01]],
dtype='interval[datetime64[ns], right]')
Specify ``start``, ``end``, and ``periods``; the frequency is generated
automatically (linearly spaced).
>>> pd.interval_range(start=0, end=6, periods=4, inclusive="right")
IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
dtype='interval[float64, right]')
The ``inclusive`` parameter specifies which endpoints of the individual
intervals within the ``IntervalIndex`` are closed.
>>> pd.interval_range(end=5, periods=4, inclusive='both')
IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]],
dtype='interval[int64, both]')
"""
inclusive, closed = _warning_interval(inclusive, closed)
if inclusive is None:
inclusive = "both"
start = maybe_box_datetimelike(start)
end = maybe_box_datetimelike(end)
endpoint = start if start is not None else end
if freq is None and com.any_none(periods, start, end):
freq = 1 if is_number(endpoint) else "D"
if com.count_not_none(start, end, periods, freq) != 3:
raise ValueError(
"Of the four parameters: start, end, periods, and "
"freq, exactly three must be specified"
)
if not _is_valid_endpoint(start):
raise ValueError(f"start must be numeric or datetime-like, got {start}")
elif not _is_valid_endpoint(end):
raise ValueError(f"end must be numeric or datetime-like, got {end}")
if is_float(periods):
periods = int(periods)
elif not is_integer(periods) and periods is not None:
raise TypeError(f"periods must be a number, got {periods}")
if freq is not None and not is_number(freq):
try:
freq = to_offset(freq)
except ValueError as err:
raise ValueError(
f"freq must be numeric or convertible to DateOffset, got {freq}"
) from err
# verify type compatibility
if not all(
[
_is_type_compatible(start, end),
_is_type_compatible(start, freq),
_is_type_compatible(end, freq),
]
):
raise TypeError("start, end, freq need to be type compatible")
# +1 to convert interval count to breaks count (n breaks = n-1 intervals)
if periods is not None:
periods += 1
breaks: np.ndarray | TimedeltaIndex | DatetimeIndex
if is_number(endpoint):
# force consistency between start/end/freq (lower end if freq skips it)
if com.all_not_none(start, end, freq):
end -= (end - start) % freq
# compute the period/start/end if unspecified (at most one)
if periods is None:
periods = int((end - start) // freq) + 1
elif start is None:
start = end - (periods - 1) * freq
elif end is None:
end = start + (periods - 1) * freq
breaks = np.linspace(start, end, periods)
if all(is_integer(x) for x in com.not_none(start, end, freq)):
# np.linspace always produces float output
breaks = maybe_downcast_numeric(breaks, np.dtype("int64"))
else:
# delegate to the appropriate range function
if isinstance(endpoint, Timestamp):
breaks = date_range(start=start, end=end, periods=periods, freq=freq)
else:
breaks = timedelta_range(start=start, end=end, periods=periods, freq=freq)
return IntervalIndex.from_breaks(breaks, name=name, inclusive=inclusive)
def __new__(
cls,
subtype=None,
inclusive: str_type | None = None,
closed: None | lib.NoDefault = lib.no_default,
):
from pandas.core.dtypes.common import (
is_string_dtype,
pandas_dtype,
)
inclusive, closed = _warning_interval(inclusive, closed)
if inclusive is not None and inclusive not in {
"right",
"left",
"both",
"neither",
}:
raise ValueError(
"inclusive must be one of 'right', 'left', 'both', 'neither'")
if isinstance(subtype, IntervalDtype):
if inclusive is not None and inclusive != subtype.inclusive:
raise ValueError(
"dtype.inclusive and 'inclusive' do not match. "
"Try IntervalDtype(dtype.subtype, inclusive) instead.")
return subtype
elif subtype is None:
# we are called as an empty constructor
# generally for pickle compat
u = object.__new__(cls)
u._subtype = None
u._closed = inclusive
return u
elif isinstance(subtype, str) and subtype.lower() == "interval":
subtype = None
else:
if isinstance(subtype, str):
m = cls._match.search(subtype)
if m is not None:
gd = m.groupdict()
subtype = gd["subtype"]
if gd.get("inclusive", None) is not None:
if inclusive is not None:
if inclusive != gd["inclusive"]:
raise ValueError(
"'inclusive' keyword does not match value "
"specified in dtype string")
inclusive = gd["inclusive"]
try:
subtype = pandas_dtype(subtype)
except TypeError as err:
raise TypeError("could not construct IntervalDtype") from err
if CategoricalDtype.is_dtype(subtype) or is_string_dtype(subtype):
# GH 19016
msg = ("category, object, and string subtypes are not supported "
"for IntervalDtype")
raise TypeError(msg)
key = str(subtype) + str(inclusive)
try:
return cls._cache_dtypes[key]
except KeyError:
u = object.__new__(cls)
u._subtype = subtype
u._closed = inclusive
cls._cache_dtypes[key] = u
return u