/
numeric.py
488 lines (406 loc) · 15.8 KB
/
numeric.py
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from __future__ import annotations
from typing import (
Callable,
Hashable,
)
import warnings
import numpy as np
from pandas._libs import (
index as libindex,
lib,
)
from pandas._typing import (
Dtype,
DtypeObj,
npt,
)
from pandas.util._decorators import (
cache_readonly,
doc,
)
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.cast import astype_nansafe
from pandas.core.dtypes.common import (
is_dtype_equal,
is_extension_array_dtype,
is_float,
is_float_dtype,
is_integer_dtype,
is_numeric_dtype,
is_scalar,
is_signed_integer_dtype,
is_unsigned_integer_dtype,
needs_i8_conversion,
pandas_dtype,
)
from pandas.core.dtypes.generic import ABCSeries
from pandas.core.indexes.base import (
Index,
maybe_extract_name,
)
class NumericIndex(Index):
"""
Immutable sequence used for indexing and alignment. The basic object
storing axis labels for all pandas objects. NumericIndex is a special case
of `Index` with purely numpy int/uint/float labels.
.. versionadded:: 1.4.0
Parameters
----------
data : array-like (1-dimensional)
dtype : NumPy dtype (default: None)
copy : bool
Make a copy of input ndarray.
name : object
Name to be stored in the index.
Attributes
----------
None
Methods
----------
None
See Also
--------
Index : The base pandas Index type.
Int64Index : Index of purely int64 labels (deprecated).
UInt64Index : Index of purely uint64 labels (deprecated).
Float64Index : Index of purely float64 labels (deprecated).
Notes
-----
An NumericIndex instance can **only** contain numpy int64/32/16/8, uint64/32/16/8 or
float64/32/16 dtype. In particular, ``NumericIndex`` *can not* hold Pandas numeric
dtypes (:class:`Int64Dtype`, :class:`Int32Dtype` etc.).
"""
_typ = "numericindex"
_values: np.ndarray
_default_dtype: np.dtype | None = None
_dtype_validation_metadata: tuple[Callable[..., bool], str] = (
is_numeric_dtype,
"numeric type",
)
_is_numeric_dtype = True
_can_hold_strings = False
_is_backward_compat_public_numeric_index: bool = True
# error: Signature of "_can_hold_na" incompatible with supertype "Index"
@cache_readonly
def _can_hold_na(self) -> bool: # type: ignore[override]
if is_float_dtype(self.dtype):
return True
else:
return False
_engine_types: dict[np.dtype, type[libindex.IndexEngine]] = {
np.dtype(np.int8): libindex.Int8Engine,
np.dtype(np.int16): libindex.Int16Engine,
np.dtype(np.int32): libindex.Int32Engine,
np.dtype(np.int64): libindex.Int64Engine,
np.dtype(np.uint8): libindex.UInt8Engine,
np.dtype(np.uint16): libindex.UInt16Engine,
np.dtype(np.uint32): libindex.UInt32Engine,
np.dtype(np.uint64): libindex.UInt64Engine,
np.dtype(np.float32): libindex.Float32Engine,
np.dtype(np.float64): libindex.Float64Engine,
}
@property
def _engine_type(self):
# error: Invalid index type "Union[dtype[Any], ExtensionDtype]" for
# "Dict[dtype[Any], Type[IndexEngine]]"; expected type "dtype[Any]"
return self._engine_types[self.dtype] # type: ignore[index]
@cache_readonly
def inferred_type(self) -> str:
return {
"i": "integer",
"u": "integer",
"f": "floating",
}[self.dtype.kind]
def __new__(cls, data=None, dtype: Dtype | None = None, copy=False, name=None):
name = maybe_extract_name(name, data, cls)
subarr = cls._ensure_array(data, dtype, copy)
return cls._simple_new(subarr, name=name)
@classmethod
def _ensure_array(cls, data, dtype, copy: bool):
"""
Ensure we have a valid array to pass to _simple_new.
"""
cls._validate_dtype(dtype)
if not isinstance(data, (np.ndarray, Index)):
# Coerce to ndarray if not already ndarray or Index
if is_scalar(data):
raise cls._scalar_data_error(data)
# other iterable of some kind
if not isinstance(data, (ABCSeries, list, tuple)):
data = list(data)
orig = data
data = np.asarray(data, dtype=dtype)
if dtype is None and data.dtype.kind == "f":
if cls is UInt64Index and (data >= 0).all():
# https://github.com/numpy/numpy/issues/19146
data = np.asarray(orig, dtype=np.uint64)
if issubclass(data.dtype.type, str):
cls._string_data_error(data)
dtype = cls._ensure_dtype(dtype)
if copy or not is_dtype_equal(data.dtype, dtype):
# TODO: the try/except below is because it's difficult to predict the error
# and/or error message from different combinations of data and dtype.
# Efforts to avoid this try/except welcome.
# See https://github.com/pandas-dev/pandas/pull/41153#discussion_r676206222
try:
subarr = np.array(data, dtype=dtype, copy=copy)
cls._validate_dtype(subarr.dtype)
except (TypeError, ValueError):
raise ValueError(f"data is not compatible with {cls.__name__}")
cls._assert_safe_casting(data, subarr)
else:
subarr = data
if subarr.ndim > 1:
# GH#13601, GH#20285, GH#27125
raise ValueError("Index data must be 1-dimensional")
subarr = np.asarray(subarr)
return subarr
@classmethod
def _validate_dtype(cls, dtype: Dtype | None) -> None:
if dtype is None:
return
validation_func, expected = cls._dtype_validation_metadata
if not validation_func(dtype):
raise ValueError(
f"Incorrect `dtype` passed: expected {expected}, received {dtype}"
)
@classmethod
def _ensure_dtype(cls, dtype: Dtype | None) -> np.dtype | None:
"""
Ensure int64 dtype for Int64Index etc. but allow int32 etc. for NumericIndex.
Assumes dtype has already been validated.
"""
if dtype is None:
return cls._default_dtype
dtype = pandas_dtype(dtype)
assert isinstance(dtype, np.dtype)
if cls._is_backward_compat_public_numeric_index:
# dtype for NumericIndex
return dtype
else:
# dtype for Int64Index, UInt64Index etc. Needed for backwards compat.
return cls._default_dtype
def __contains__(self, key) -> bool:
"""
Check if key is a float and has a decimal. If it has, return False.
"""
if not is_integer_dtype(self.dtype):
return super().__contains__(key)
hash(key)
try:
if is_float(key) and int(key) != key:
# otherwise the `key in self._engine` check casts e.g. 1.1 -> 1
return False
return key in self._engine
except (OverflowError, TypeError, ValueError):
return False
@doc(Index.astype)
def astype(self, dtype, copy: bool = True):
dtype = pandas_dtype(dtype)
if is_float_dtype(self.dtype):
if needs_i8_conversion(dtype):
raise TypeError(
f"Cannot convert Float64Index to dtype {dtype}; integer "
"values are required for conversion"
)
elif is_integer_dtype(dtype) and not is_extension_array_dtype(dtype):
# TODO(ExtensionIndex); this can change once we have an EA Index type
# GH 13149
arr = astype_nansafe(self._values, dtype=dtype)
if isinstance(self, Float64Index):
return Int64Index(arr, name=self.name)
else:
return NumericIndex(arr, name=self.name, dtype=dtype)
elif self._is_backward_compat_public_numeric_index:
# this block is needed so e.g. NumericIndex[int8].astype("int32") returns
# NumericIndex[int32] and not Int64Index with dtype int64.
# When Int64Index etc. are removed from the code base, removed this also.
if not is_extension_array_dtype(dtype) and is_numeric_dtype(dtype):
return self._constructor(self, dtype=dtype, copy=copy)
return super().astype(dtype, copy=copy)
# ----------------------------------------------------------------
# Indexing Methods
# error: Decorated property not supported
@cache_readonly # type: ignore[misc]
@doc(Index._should_fallback_to_positional)
def _should_fallback_to_positional(self) -> bool:
return False
@doc(Index._convert_slice_indexer)
def _convert_slice_indexer(self, key: slice, kind: str):
if is_float_dtype(self.dtype):
assert kind in ["loc", "getitem"]
# We always treat __getitem__ slicing as label-based
# translate to locations
return self.slice_indexer(key.start, key.stop, key.step)
return super()._convert_slice_indexer(key, kind=kind)
@doc(Index._maybe_cast_slice_bound)
def _maybe_cast_slice_bound(self, label, side: str, kind=lib.no_default):
assert kind in ["loc", "getitem", None, lib.no_default]
self._deprecated_arg(kind, "kind", "_maybe_cast_slice_bound")
# we will try to coerce to integers
return self._maybe_cast_indexer(label)
# ----------------------------------------------------------------
@doc(Index._shallow_copy)
def _shallow_copy(self, values, name: Hashable = lib.no_default):
if not self._can_hold_na and values.dtype.kind == "f":
name = self._name if name is lib.no_default else name
# Ensure we are not returning an Int64Index with float data:
return Float64Index._simple_new(values, name=name)
return super()._shallow_copy(values=values, name=name)
def _convert_tolerance(self, tolerance, target):
tolerance = super()._convert_tolerance(tolerance, target)
if not np.issubdtype(tolerance.dtype, np.number):
if tolerance.ndim > 0:
raise ValueError(
f"tolerance argument for {type(self).__name__} must contain "
"numeric elements if it is list type"
)
else:
raise ValueError(
f"tolerance argument for {type(self).__name__} must be numeric "
f"if it is a scalar: {repr(tolerance)}"
)
return tolerance
def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
# If we ever have BoolIndex or ComplexIndex, this may need to be tightened
return is_numeric_dtype(dtype)
@classmethod
def _assert_safe_casting(cls, data: np.ndarray, subarr: np.ndarray) -> None:
"""
Ensure incoming data can be represented with matching signed-ness.
Needed if the process of casting data from some accepted dtype to the internal
dtype(s) bears the risk of truncation (e.g. float to int).
"""
if is_integer_dtype(subarr.dtype):
if not np.array_equal(data, subarr):
raise TypeError("Unsafe NumPy casting, you must explicitly cast")
@property
def _is_all_dates(self) -> bool:
"""
Checks that all the labels are datetime objects.
"""
return False
def _format_native_types(
self, *, na_rep="", float_format=None, decimal=".", quoting=None, **kwargs
):
from pandas.io.formats.format import FloatArrayFormatter
if is_float_dtype(self.dtype):
formatter = FloatArrayFormatter(
self._values,
na_rep=na_rep,
float_format=float_format,
decimal=decimal,
quoting=quoting,
fixed_width=False,
)
return formatter.get_result_as_array()
return super()._format_native_types(
na_rep=na_rep,
float_format=float_format,
decimal=decimal,
quoting=quoting,
**kwargs,
)
_num_index_shared_docs = {}
_num_index_shared_docs[
"class_descr"
] = """
Immutable sequence used for indexing and alignment. The basic object
storing axis labels for all pandas objects. %(klass)s is a special case
of `Index` with purely %(ltype)s labels. %(extra)s.
.. deprecated:: 1.4.0
In pandas v2.0 %(klass)s will be removed and :class:`NumericIndex` used instead.
%(klass)s will remain fully functional for the duration of pandas 1.x.
Parameters
----------
data : array-like (1-dimensional)
dtype : NumPy dtype (default: %(dtype)s)
copy : bool
Make a copy of input ndarray.
name : object
Name to be stored in the index.
Attributes
----------
None
Methods
----------
None
See Also
--------
Index : The base pandas Index type.
NumericIndex : Index of numpy int/uint/float data.
Notes
-----
An Index instance can **only** contain hashable objects.
"""
class IntegerIndex(NumericIndex):
"""
This is an abstract class for Int64Index, UInt64Index.
"""
_is_backward_compat_public_numeric_index: bool = False
@property
def asi8(self) -> npt.NDArray[np.int64]:
# do not cache or you'll create a memory leak
warnings.warn(
"Index.asi8 is deprecated and will be removed in a future version.",
FutureWarning,
stacklevel=find_stack_level(),
)
return self._values.view(self._default_dtype)
def _validate_fill_value(self, value):
# e.g. np.array([1.0]) we want np.array([1], dtype=self.dtype)
# see TestSetitemFloatNDarrayIntoIntegerSeries
super()._validate_fill_value(value)
if hasattr(value, "dtype") and is_float_dtype(value.dtype):
converted = value.astype(self.dtype)
if (converted == value).all():
# See also: can_hold_element
return converted
raise TypeError
return value
class Int64Index(IntegerIndex):
_index_descr_args = {
"klass": "Int64Index",
"ltype": "integer",
"dtype": "int64",
"extra": "",
}
__doc__ = _num_index_shared_docs["class_descr"] % _index_descr_args
_typ = "int64index"
_engine_type = libindex.Int64Engine
_default_dtype = np.dtype(np.int64)
_dtype_validation_metadata = (is_signed_integer_dtype, "signed integer")
class UInt64Index(IntegerIndex):
_index_descr_args = {
"klass": "UInt64Index",
"ltype": "unsigned integer",
"dtype": "uint64",
"extra": "",
}
__doc__ = _num_index_shared_docs["class_descr"] % _index_descr_args
_typ = "uint64index"
_engine_type = libindex.UInt64Engine
_default_dtype = np.dtype(np.uint64)
_dtype_validation_metadata = (is_unsigned_integer_dtype, "unsigned integer")
def _validate_fill_value(self, value):
# e.g. np.array([1]) we want np.array([1], dtype=np.uint64)
# see test_where_uin64
super()._validate_fill_value(value)
if hasattr(value, "dtype") and is_signed_integer_dtype(value.dtype):
if (value >= 0).all():
return value.astype(self.dtype)
raise TypeError
return value
class Float64Index(NumericIndex):
_index_descr_args = {
"klass": "Float64Index",
"dtype": "float64",
"ltype": "float",
"extra": "",
}
__doc__ = _num_index_shared_docs["class_descr"] % _index_descr_args
_typ = "float64index"
_engine_type = libindex.Float64Engine
_default_dtype = np.dtype(np.float64)
_dtype_validation_metadata = (is_float_dtype, "float")
_is_backward_compat_public_numeric_index: bool = False