| Current File : //usr/local/lib64/python3.6/site-packages/pandas/core/indexes/range.py |
from datetime import timedelta
import operator
from sys import getsizeof
from typing import Any, List, Optional
import warnings
import numpy as np
from pandas._libs import index as libindex
from pandas._libs.lib import no_default
from pandas._typing import Label
import pandas.compat as compat
from pandas.compat.numpy import function as nv
from pandas.util._decorators import Appender, cache_readonly, doc
from pandas.core.dtypes.common import (
ensure_platform_int,
ensure_python_int,
is_float,
is_integer,
is_integer_dtype,
is_list_like,
is_scalar,
is_timedelta64_dtype,
)
from pandas.core.dtypes.generic import ABCTimedeltaIndex
from pandas.core import ops
import pandas.core.common as com
from pandas.core.construction import extract_array
import pandas.core.indexes.base as ibase
from pandas.core.indexes.base import _index_shared_docs, maybe_extract_name
from pandas.core.indexes.numeric import Int64Index
from pandas.core.ops.common import unpack_zerodim_and_defer
_empty_range = range(0)
class RangeIndex(Int64Index):
"""
Immutable Index implementing a monotonic integer range.
RangeIndex is a memory-saving special case of Int64Index limited to
representing monotonic ranges. Using RangeIndex may in some instances
improve computing speed.
This is the default index type used
by DataFrame and Series when no explicit index is provided by the user.
Parameters
----------
start : int (default: 0), or other RangeIndex instance
If int and "stop" is not given, interpreted as "stop" instead.
stop : int (default: 0)
step : int (default: 1)
name : object, optional
Name to be stored in the index.
copy : bool, default False
Unused, accepted for homogeneity with other index types.
Attributes
----------
start
stop
step
Methods
-------
from_range
See Also
--------
Index : The base pandas Index type.
Int64Index : Index of int64 data.
"""
_typ = "rangeindex"
_engine_type = libindex.Int64Engine
_range: range
# check whether self._data has been called
_cached_data: Optional[np.ndarray] = None
# --------------------------------------------------------------------
# Constructors
def __new__(
cls, start=None, stop=None, step=None, dtype=None, copy=False, name=None,
):
cls._validate_dtype(dtype)
name = maybe_extract_name(name, start, cls)
# RangeIndex
if isinstance(start, RangeIndex):
start = start._range
return cls._simple_new(start, name=name)
# validate the arguments
if com.all_none(start, stop, step):
raise TypeError("RangeIndex(...) must be called with integers")
start = ensure_python_int(start) if start is not None else 0
if stop is None:
start, stop = 0, start
else:
stop = ensure_python_int(stop)
step = ensure_python_int(step) if step is not None else 1
if step == 0:
raise ValueError("Step must not be zero")
rng = range(start, stop, step)
return cls._simple_new(rng, name=name)
@classmethod
def from_range(cls, data: range, name=None, dtype=None) -> "RangeIndex":
"""
Create RangeIndex from a range object.
Returns
-------
RangeIndex
"""
if not isinstance(data, range):
raise TypeError(
f"{cls.__name__}(...) must be called with object coercible to a "
f"range, {repr(data)} was passed"
)
cls._validate_dtype(dtype)
return cls._simple_new(data, name=name)
@classmethod
def _simple_new(cls, values: range, name: Label = None) -> "RangeIndex":
result = object.__new__(cls)
assert isinstance(values, range)
result._range = values
result.name = name
result._cache = {}
result._reset_identity()
return result
# --------------------------------------------------------------------
@cache_readonly
def _constructor(self):
""" return the class to use for construction """
return Int64Index
@property
def _data(self):
"""
An int array that for performance reasons is created only when needed.
The constructed array is saved in ``_cached_data``. This allows us to
check if the array has been created without accessing ``_data`` and
triggering the construction.
"""
if self._cached_data is None:
self._cached_data = np.arange(
self.start, self.stop, self.step, dtype=np.int64
)
return self._cached_data
@cache_readonly
def _int64index(self) -> Int64Index:
return Int64Index._simple_new(self._data, name=self.name)
def _get_data_as_items(self):
""" return a list of tuples of start, stop, step """
rng = self._range
return [("start", rng.start), ("stop", rng.stop), ("step", rng.step)]
def __reduce__(self):
d = self._get_attributes_dict()
d.update(dict(self._get_data_as_items()))
return ibase._new_Index, (type(self), d), None
# --------------------------------------------------------------------
# Rendering Methods
def _format_attrs(self):
"""
Return a list of tuples of the (attr, formatted_value)
"""
attrs = self._get_data_as_items()
if self.name is not None:
attrs.append(("name", ibase.default_pprint(self.name)))
return attrs
def _format_data(self, name=None):
# we are formatting thru the attributes
return None
def _format_with_header(self, header: List[str], na_rep: str = "NaN") -> List[str]:
if not len(self._range):
return header
first_val_str = str(self._range[0])
last_val_str = str(self._range[-1])
max_length = max(len(first_val_str), len(last_val_str))
return header + [f"{x:<{max_length}}" for x in self._range]
# --------------------------------------------------------------------
_deprecation_message = (
"RangeIndex.{} is deprecated and will be "
"removed in a future version. Use RangeIndex.{} "
"instead"
)
@cache_readonly
def start(self):
"""
The value of the `start` parameter (``0`` if this was not supplied).
"""
# GH 25710
return self._range.start
@property
def _start(self):
"""
The value of the `start` parameter (``0`` if this was not supplied).
.. deprecated:: 0.25.0
Use ``start`` instead.
"""
warnings.warn(
self._deprecation_message.format("_start", "start"),
FutureWarning,
stacklevel=2,
)
return self.start
@cache_readonly
def stop(self):
"""
The value of the `stop` parameter.
"""
return self._range.stop
@property
def _stop(self):
"""
The value of the `stop` parameter.
.. deprecated:: 0.25.0
Use ``stop`` instead.
"""
# GH 25710
warnings.warn(
self._deprecation_message.format("_stop", "stop"),
FutureWarning,
stacklevel=2,
)
return self.stop
@cache_readonly
def step(self):
"""
The value of the `step` parameter (``1`` if this was not supplied).
"""
# GH 25710
return self._range.step
@property
def _step(self):
"""
The value of the `step` parameter (``1`` if this was not supplied).
.. deprecated:: 0.25.0
Use ``step`` instead.
"""
# GH 25710
warnings.warn(
self._deprecation_message.format("_step", "step"),
FutureWarning,
stacklevel=2,
)
return self.step
@cache_readonly
def nbytes(self) -> int:
"""
Return the number of bytes in the underlying data.
"""
rng = self._range
return getsizeof(rng) + sum(
getsizeof(getattr(rng, attr_name))
for attr_name in ["start", "stop", "step"]
)
def memory_usage(self, deep: bool = False) -> int:
"""
Memory usage of my values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False
See Also
--------
numpy.ndarray.nbytes
"""
return self.nbytes
@property
def dtype(self) -> np.dtype:
return np.dtype(np.int64)
@property
def is_unique(self) -> bool:
""" return if the index has unique values """
return True
@cache_readonly
def is_monotonic_increasing(self) -> bool:
return self._range.step > 0 or len(self) <= 1
@cache_readonly
def is_monotonic_decreasing(self) -> bool:
return self._range.step < 0 or len(self) <= 1
@property
def has_duplicates(self) -> bool:
return False
def __contains__(self, key: Any) -> bool:
hash(key)
try:
key = ensure_python_int(key)
except TypeError:
return False
return key in self._range
@doc(Int64Index.get_loc)
def get_loc(self, key, method=None, tolerance=None):
if method is None and tolerance is None:
if is_integer(key) or (is_float(key) and key.is_integer()):
new_key = int(key)
try:
return self._range.index(new_key)
except ValueError as err:
raise KeyError(key) from err
raise KeyError(key)
return super().get_loc(key, method=method, tolerance=tolerance)
@Appender(_index_shared_docs["get_indexer"])
def get_indexer(self, target, method=None, limit=None, tolerance=None):
if com.any_not_none(method, tolerance, limit) or not is_list_like(target):
return super().get_indexer(
target, method=method, tolerance=tolerance, limit=limit
)
if self.step > 0:
start, stop, step = self.start, self.stop, self.step
else:
# GH 28678: work on reversed range for simplicity
reverse = self._range[::-1]
start, stop, step = reverse.start, reverse.stop, reverse.step
target_array = np.asarray(target)
if not (is_integer_dtype(target_array) and target_array.ndim == 1):
# checks/conversions/roundings are delegated to general method
return super().get_indexer(target, method=method, tolerance=tolerance)
locs = target_array - start
valid = (locs % step == 0) & (locs >= 0) & (target_array < stop)
locs[~valid] = -1
locs[valid] = locs[valid] / step
if step != self.step:
# We reversed this range: transform to original locs
locs[valid] = len(self) - 1 - locs[valid]
return ensure_platform_int(locs)
def tolist(self):
return list(self._range)
@doc(Int64Index._shallow_copy)
def _shallow_copy(self, values=None, name: Label = no_default):
name = self.name if name is no_default else name
if values is None:
result = self._simple_new(self._range, name=name)
result._cache = self._cache.copy()
return result
else:
return Int64Index._simple_new(values, name=name)
@doc(Int64Index.copy)
def copy(self, name=None, deep=False, dtype=None, **kwargs):
self._validate_dtype(dtype)
if name is None:
name = self.name
return self.from_range(self._range, name=name)
def _minmax(self, meth: str):
no_steps = len(self) - 1
if no_steps == -1:
return np.nan
elif (meth == "min" and self.step > 0) or (meth == "max" and self.step < 0):
return self.start
return self.start + self.step * no_steps
def min(self, axis=None, skipna=True, *args, **kwargs) -> int:
"""The minimum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
nv.validate_min(args, kwargs)
return self._minmax("min")
def max(self, axis=None, skipna=True, *args, **kwargs) -> int:
"""The maximum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
nv.validate_max(args, kwargs)
return self._minmax("max")
def argsort(self, *args, **kwargs) -> np.ndarray:
"""
Returns the indices that would sort the index and its
underlying data.
Returns
-------
argsorted : numpy array
See Also
--------
numpy.ndarray.argsort
"""
nv.validate_argsort(args, kwargs)
if self._range.step > 0:
return np.arange(len(self))
else:
return np.arange(len(self) - 1, -1, -1)
def equals(self, other) -> bool:
"""
Determines if two Index objects contain the same elements.
"""
if isinstance(other, RangeIndex):
return self._range == other._range
return super().equals(other)
def intersection(self, other, sort=False):
"""
Form the intersection of two Index objects.
Parameters
----------
other : Index or array-like
sort : False or None, default False
Sort the resulting index if possible
.. versionadded:: 0.24.0
.. versionchanged:: 0.24.1
Changed the default to ``False`` to match the behaviour
from before 0.24.0.
Returns
-------
intersection : Index
"""
self._validate_sort_keyword(sort)
if self.equals(other):
return self._get_reconciled_name_object(other)
if not isinstance(other, RangeIndex):
return super().intersection(other, sort=sort)
if not len(self) or not len(other):
return self._simple_new(_empty_range)
first = self._range[::-1] if self.step < 0 else self._range
second = other._range[::-1] if other.step < 0 else other._range
# check whether intervals intersect
# deals with in- and decreasing ranges
int_low = max(first.start, second.start)
int_high = min(first.stop, second.stop)
if int_high <= int_low:
return self._simple_new(_empty_range)
# Method hint: linear Diophantine equation
# solve intersection problem
# performance hint: for identical step sizes, could use
# cheaper alternative
gcd, s, t = self._extended_gcd(first.step, second.step)
# check whether element sets intersect
if (first.start - second.start) % gcd:
return self._simple_new(_empty_range)
# calculate parameters for the RangeIndex describing the
# intersection disregarding the lower bounds
tmp_start = first.start + (second.start - first.start) * first.step // gcd * s
new_step = first.step * second.step // gcd
new_range = range(tmp_start, int_high, new_step)
new_index = self._simple_new(new_range)
# adjust index to limiting interval
new_start = new_index._min_fitting_element(int_low)
new_range = range(new_start, new_index.stop, new_index.step)
new_index = self._simple_new(new_range)
if (self.step < 0 and other.step < 0) is not (new_index.step < 0):
new_index = new_index[::-1]
if sort is None:
new_index = new_index.sort_values()
return new_index
def _min_fitting_element(self, lower_limit: int) -> int:
"""Returns the smallest element greater than or equal to the limit"""
no_steps = -(-(lower_limit - self.start) // abs(self.step))
return self.start + abs(self.step) * no_steps
def _max_fitting_element(self, upper_limit: int) -> int:
"""Returns the largest element smaller than or equal to the limit"""
no_steps = (upper_limit - self.start) // abs(self.step)
return self.start + abs(self.step) * no_steps
def _extended_gcd(self, a, b):
"""
Extended Euclidean algorithms to solve Bezout's identity:
a*x + b*y = gcd(x, y)
Finds one particular solution for x, y: s, t
Returns: gcd, s, t
"""
s, old_s = 0, 1
t, old_t = 1, 0
r, old_r = b, a
while r:
quotient = old_r // r
old_r, r = r, old_r - quotient * r
old_s, s = s, old_s - quotient * s
old_t, t = t, old_t - quotient * t
return old_r, old_s, old_t
def _union(self, other, sort):
"""
Form the union of two Index objects and sorts if possible
Parameters
----------
other : Index or array-like
sort : False or None, default None
Whether to sort resulting index. ``sort=None`` returns a
monotonically increasing ``RangeIndex`` if possible or a sorted
``Int64Index`` if not. ``sort=False`` always returns an
unsorted ``Int64Index``
.. versionadded:: 0.25.0
Returns
-------
union : Index
"""
if not len(other) or self.equals(other) or not len(self):
return super()._union(other, sort=sort)
if isinstance(other, RangeIndex) and sort is None:
start_s, step_s = self.start, self.step
end_s = self.start + self.step * (len(self) - 1)
start_o, step_o = other.start, other.step
end_o = other.start + other.step * (len(other) - 1)
if self.step < 0:
start_s, step_s, end_s = end_s, -step_s, start_s
if other.step < 0:
start_o, step_o, end_o = end_o, -step_o, start_o
if len(self) == 1 and len(other) == 1:
step_s = step_o = abs(self.start - other.start)
elif len(self) == 1:
step_s = step_o
elif len(other) == 1:
step_o = step_s
start_r = min(start_s, start_o)
end_r = max(end_s, end_o)
if step_o == step_s:
if (
(start_s - start_o) % step_s == 0
and (start_s - end_o) <= step_s
and (start_o - end_s) <= step_s
):
return type(self)(start_r, end_r + step_s, step_s)
if (
(step_s % 2 == 0)
and (abs(start_s - start_o) <= step_s / 2)
and (abs(end_s - end_o) <= step_s / 2)
):
return type(self)(start_r, end_r + step_s / 2, step_s / 2)
elif step_o % step_s == 0:
if (
(start_o - start_s) % step_s == 0
and (start_o + step_s >= start_s)
and (end_o - step_s <= end_s)
):
return type(self)(start_r, end_r + step_s, step_s)
elif step_s % step_o == 0:
if (
(start_s - start_o) % step_o == 0
and (start_s + step_o >= start_o)
and (end_s - step_o <= end_o)
):
return type(self)(start_r, end_r + step_o, step_o)
return self._int64index._union(other, sort=sort)
@doc(Int64Index.join)
def join(self, other, how="left", level=None, return_indexers=False, sort=False):
if how == "outer" and self is not other:
# note: could return RangeIndex in more circumstances
return self._int64index.join(other, how, level, return_indexers, sort)
return super().join(other, how, level, return_indexers, sort)
def _concat(self, indexes, name):
"""
Overriding parent method for the case of all RangeIndex instances.
When all members of "indexes" are of type RangeIndex: result will be
RangeIndex if possible, Int64Index otherwise. E.g.:
indexes = [RangeIndex(3), RangeIndex(3, 6)] -> RangeIndex(6)
indexes = [RangeIndex(3), RangeIndex(4, 6)] -> Int64Index([0,1,2,4,5])
"""
if not all(isinstance(x, RangeIndex) for x in indexes):
return super()._concat(indexes, name)
start = step = next_ = None
# Filter the empty indexes
non_empty_indexes = [obj for obj in indexes if len(obj)]
for obj in non_empty_indexes:
rng: range = obj._range
if start is None:
# This is set by the first non-empty index
start = rng.start
if step is None and len(rng) > 1:
step = rng.step
elif step is None:
# First non-empty index had only one element
if rng.start == start:
result = Int64Index(np.concatenate([x._values for x in indexes]))
return result.rename(name)
step = rng.start - start
non_consecutive = (step != rng.step and len(rng) > 1) or (
next_ is not None and rng.start != next_
)
if non_consecutive:
result = Int64Index(np.concatenate([x._values for x in indexes]))
return result.rename(name)
if step is not None:
next_ = rng[-1] + step
if non_empty_indexes:
# Get the stop value from "next" or alternatively
# from the last non-empty index
stop = non_empty_indexes[-1].stop if next_ is None else next_
return RangeIndex(start, stop, step).rename(name)
# Here all "indexes" had 0 length, i.e. were empty.
# In this case return an empty range index.
return RangeIndex(0, 0).rename(name)
def __len__(self) -> int:
"""
return the length of the RangeIndex
"""
return len(self._range)
@property
def size(self) -> int:
return len(self)
def __getitem__(self, key):
"""
Conserve RangeIndex type for scalar and slice keys.
"""
if isinstance(key, slice):
new_range = self._range[key]
return self._simple_new(new_range, name=self.name)
elif is_integer(key):
new_key = int(key)
try:
return self._range[new_key]
except IndexError as err:
raise IndexError(
f"index {key} is out of bounds for axis 0 with size {len(self)}"
) from err
elif is_scalar(key):
raise IndexError(
"only integers, slices (`:`), "
"ellipsis (`...`), numpy.newaxis (`None`) "
"and integer or boolean "
"arrays are valid indices"
)
# fall back to Int64Index
return super().__getitem__(key)
@unpack_zerodim_and_defer("__floordiv__")
def __floordiv__(self, other):
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
new_range = range(start, stop, step or 1)
return self._simple_new(new_range, name=self.name)
if len(self) == 1:
start = self.start // other
new_range = range(start, start + 1, 1)
return self._simple_new(new_range, name=self.name)
return self._int64index // other
def all(self) -> bool:
return 0 not in self._range
def any(self) -> bool:
return any(self._range)
@classmethod
def _add_numeric_methods_binary(cls):
""" add in numeric methods, specialized to RangeIndex """
def _make_evaluate_binop(op, step=False):
"""
Parameters
----------
op : callable that accepts 2 params
perform the binary op
step : callable, optional, default to False
op to apply to the step parm if not None
if False, use the existing step
"""
@unpack_zerodim_and_defer(op.__name__)
def _evaluate_numeric_binop(self, other):
if isinstance(other, ABCTimedeltaIndex):
# Defer to TimedeltaIndex implementation
return NotImplemented
elif isinstance(other, (timedelta, np.timedelta64)):
# GH#19333 is_integer evaluated True on timedelta64,
# so we need to catch these explicitly
return op(self._int64index, other)
elif is_timedelta64_dtype(other):
# Must be an np.ndarray; GH#22390
return op(self._int64index, other)
other = extract_array(other, extract_numpy=True)
attrs = self._get_attributes_dict()
left, right = self, other
try:
# apply if we have an override
if step:
with np.errstate(all="ignore"):
rstep = step(left.step, right)
# we don't have a representable op
# so return a base index
if not is_integer(rstep) or not rstep:
raise ValueError
else:
rstep = left.step
with np.errstate(all="ignore"):
rstart = op(left.start, right)
rstop = op(left.stop, right)
result = type(self)(rstart, rstop, rstep, **attrs)
# for compat with numpy / Int64Index
# even if we can represent as a RangeIndex, return
# as a Float64Index if we have float-like descriptors
if not all(is_integer(x) for x in [rstart, rstop, rstep]):
result = result.astype("float64")
return result
except (ValueError, TypeError, ZeroDivisionError):
# Defer to Int64Index implementation
return op(self._int64index, other)
# TODO: Do attrs get handled reliably?
name = f"__{op.__name__}__"
return compat.set_function_name(_evaluate_numeric_binop, name, cls)
cls.__add__ = _make_evaluate_binop(operator.add)
cls.__radd__ = _make_evaluate_binop(ops.radd)
cls.__sub__ = _make_evaluate_binop(operator.sub)
cls.__rsub__ = _make_evaluate_binop(ops.rsub)
cls.__mul__ = _make_evaluate_binop(operator.mul, step=operator.mul)
cls.__rmul__ = _make_evaluate_binop(ops.rmul, step=ops.rmul)
cls.__truediv__ = _make_evaluate_binop(operator.truediv, step=operator.truediv)
cls.__rtruediv__ = _make_evaluate_binop(ops.rtruediv, step=ops.rtruediv)
RangeIndex._add_numeric_methods()