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"""An interface for extending pandas with custom arrays.
.. warning::
This is an experimental API and subject to breaking changes
without warning.
"""
import operator
from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union
import numpy as np
from pandas.compat import set_function_name
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
from pandas.util._decorators import Appender, Substitution
from pandas.util._validators import validate_fillna_kwargs
from pandas.core.dtypes.common import is_array_like, is_list_like
from pandas.core.dtypes.dtypes import ExtensionDtype
from pandas.core.dtypes.generic import ABCExtensionArray, ABCIndexClass, ABCSeries
from pandas.core.dtypes.missing import isna
from pandas._typing import ArrayLike
from pandas.core import ops
from pandas.core.algorithms import _factorize_array, unique
from pandas.core.missing import backfill_1d, pad_1d
from pandas.core.sorting import nargsort
_not_implemented_message = "{} does not implement {}."
_extension_array_shared_docs = dict() # type: Dict[str, str]
class ExtensionArray:
"""
Abstract base class for custom 1-D array types.
pandas will recognize instances of this class as proper arrays
with a custom type and will not attempt to coerce them to objects. They
may be stored directly inside a :class:`DataFrame` or :class:`Series`.
.. versionadded:: 0.23.0
Attributes
----------
dtype
nbytes
ndim
shape
Methods
-------
argsort
astype
copy
dropna
factorize
fillna
isna
ravel
repeat
searchsorted
shift
take
unique
view
_concat_same_type
_formatter
_from_factorized
_from_sequence
_from_sequence_of_strings
_ndarray_values
_reduce
_values_for_argsort
_values_for_factorize
Notes
-----
The interface includes the following abstract methods that must be
implemented by subclasses:
* _from_sequence
* _from_factorized
* __getitem__
* __len__
* dtype
* nbytes
* isna
* take
* copy
* _concat_same_type
A default repr displaying the type, (truncated) data, length,
and dtype is provided. It can be customized or replaced by
by overriding:
* __repr__ : A default repr for the ExtensionArray.
* _formatter : Print scalars inside a Series or DataFrame.
Some methods require casting the ExtensionArray to an ndarray of Python
objects with ``self.astype(object)``, which may be expensive. When
performance is a concern, we highly recommend overriding the following
methods:
* fillna
* dropna
* unique
* factorize / _values_for_factorize
* argsort / _values_for_argsort
* searchsorted
The remaining methods implemented on this class should be performant,
as they only compose abstract methods. Still, a more efficient
implementation may be available, and these methods can be overridden.
One can implement methods to handle array reductions.
* _reduce
One can implement methods to handle parsing from strings that will be used
in methods such as ``pandas.io.parsers.read_csv``.
* _from_sequence_of_strings
This class does not inherit from 'abc.ABCMeta' for performance reasons.
Methods and properties required by the interface raise
``pandas.errors.AbstractMethodError`` and no ``register`` method is
provided for registering virtual subclasses.
ExtensionArrays are limited to 1 dimension.
They may be backed by none, one, or many NumPy arrays. For example,
``pandas.Categorical`` is an extension array backed by two arrays,
one for codes and one for categories. An array of IPv6 address may
be backed by a NumPy structured array with two fields, one for the
lower 64 bits and one for the upper 64 bits. Or they may be backed
by some other storage type, like Python lists. Pandas makes no
assumptions on how the data are stored, just that it can be converted
to a NumPy array.
The ExtensionArray interface does not impose any rules on how this data
is stored. However, currently, the backing data cannot be stored in
attributes called ``.values`` or ``._values`` to ensure full compatibility
with pandas internals. But other names as ``.data``, ``._data``,
``._items``, ... can be freely used.
If implementing NumPy's ``__array_ufunc__`` interface, pandas expects
that
1. You defer by returning ``NotImplemented`` when any Series are present
in `inputs`. Pandas will extract the arrays and call the ufunc again.
2. You define a ``_HANDLED_TYPES`` tuple as an attribute on the class.
Pandas inspect this to determine whether the ufunc is valid for the
types present.
See :ref:`extending.extension.ufunc` for more.
"""
# '_typ' is for pandas.core.dtypes.generic.ABCExtensionArray.
# Don't override this.
_typ = "extension"
# ------------------------------------------------------------------------
# Constructors
# ------------------------------------------------------------------------
@classmethod
def _from_sequence(cls, scalars, dtype=None, copy=False):
"""
Construct a new ExtensionArray from a sequence of scalars.
Parameters
----------
scalars : Sequence
Each element will be an instance of the scalar type for this
array, ``cls.dtype.type``.
dtype : dtype, optional
Construct for this particular dtype. This should be a Dtype
compatible with the ExtensionArray.
copy : boolean, default False
If True, copy the underlying data.
Returns
-------
ExtensionArray
"""
raise AbstractMethodError(cls)
@classmethod
def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
"""Construct a new ExtensionArray from a sequence of strings.
.. versionadded:: 0.24.0
Parameters
----------
strings : Sequence
Each element will be an instance of the scalar type for this
array, ``cls.dtype.type``.
dtype : dtype, optional
Construct for this particular dtype. This should be a Dtype
compatible with the ExtensionArray.
copy : boolean, default False
If True, copy the underlying data.
Returns
-------
ExtensionArray
"""
raise AbstractMethodError(cls)
@classmethod
def _from_factorized(cls, values, original):
"""
Reconstruct an ExtensionArray after factorization.
Parameters
----------
values : ndarray
An integer ndarray with the factorized values.
original : ExtensionArray
The original ExtensionArray that factorize was called on.
See Also
--------
factorize
ExtensionArray.factorize
"""
raise AbstractMethodError(cls)
# ------------------------------------------------------------------------
# Must be a Sequence
# ------------------------------------------------------------------------
def __getitem__(self, item):
# type (Any) -> Any
"""
Select a subset of self.
Parameters
----------
item : int, slice, or ndarray
* int: The position in 'self' to get.
* slice: A slice object, where 'start', 'stop', and 'step' are
integers or None
* ndarray: A 1-d boolean NumPy ndarray the same length as 'self'
Returns
-------
item : scalar or ExtensionArray
Notes
-----
For scalar ``item``, return a scalar value suitable for the array's
type. This should be an instance of ``self.dtype.type``.
For slice ``key``, return an instance of ``ExtensionArray``, even
if the slice is length 0 or 1.
For a boolean mask, return an instance of ``ExtensionArray``, filtered
to the values where ``item`` is True.
"""
raise AbstractMethodError(self)
def __setitem__(self, key: Union[int, np.ndarray], value: Any) -> None:
"""
Set one or more values inplace.
This method is not required to satisfy the pandas extension array
interface.
Parameters
----------
key : int, ndarray, or slice
When called from, e.g. ``Series.__setitem__``, ``key`` will be
one of
* scalar int
* ndarray of integers.
* boolean ndarray
* slice object
value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
value or values to be set of ``key``.
Returns
-------
None
"""
# Some notes to the ExtensionArray implementor who may have ended up
# here. While this method is not required for the interface, if you
# *do* choose to implement __setitem__, then some semantics should be
# observed:
#
# * Setting multiple values : ExtensionArrays should support setting
# multiple values at once, 'key' will be a sequence of integers and
# 'value' will be a same-length sequence.
#
# * Broadcasting : For a sequence 'key' and a scalar 'value',
# each position in 'key' should be set to 'value'.
#
# * Coercion : Most users will expect basic coercion to work. For
# example, a string like '2018-01-01' is coerced to a datetime
# when setting on a datetime64ns array. In general, if the
# __init__ method coerces that value, then so should __setitem__
# Note, also, that Series/DataFrame.where internally use __setitem__
# on a copy of the data.
raise NotImplementedError(
_not_implemented_message.format(type(self), "__setitem__")
)
def __len__(self) -> int:
"""
Length of this array
Returns
-------
length : int
"""
raise AbstractMethodError(self)
def __iter__(self):
"""
Iterate over elements of the array.
"""
# This needs to be implemented so that pandas recognizes extension
# arrays as list-like. The default implementation makes successive
# calls to ``__getitem__``, which may be slower than necessary.
for i in range(len(self)):
yield self[i]
# ------------------------------------------------------------------------
# Required attributes
# ------------------------------------------------------------------------
@property
def dtype(self) -> ExtensionDtype:
"""
An instance of 'ExtensionDtype'.
"""
raise AbstractMethodError(self)
@property
def shape(self) -> Tuple[int, ...]:
"""
Return a tuple of the array dimensions.
"""
return (len(self),)
@property
def ndim(self) -> int:
"""
Extension Arrays are only allowed to be 1-dimensional.
"""
return 1
@property
def nbytes(self) -> int:
"""
The number of bytes needed to store this object in memory.
"""
# If this is expensive to compute, return an approximate lower bound
# on the number of bytes needed.
raise AbstractMethodError(self)
# ------------------------------------------------------------------------
# Additional Methods
# ------------------------------------------------------------------------
def astype(self, dtype, copy=True):
"""
Cast to a NumPy array with 'dtype'.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
copy : bool, default True
Whether to copy the data, even if not necessary. If False,
a copy is made only if the old dtype does not match the
new dtype.
Returns
-------
array : ndarray
NumPy ndarray with 'dtype' for its dtype.
"""
return np.array(self, dtype=dtype, copy=copy)
def isna(self) -> ArrayLike:
"""
A 1-D array indicating if each value is missing.
Returns
-------
na_values : Union[np.ndarray, ExtensionArray]
In most cases, this should return a NumPy ndarray. For
exceptional cases like ``SparseArray``, where returning
an ndarray would be expensive, an ExtensionArray may be
returned.
Notes
-----
If returning an ExtensionArray, then
* ``na_values._is_boolean`` should be True
* `na_values` should implement :func:`ExtensionArray._reduce`
* ``na_values.any`` and ``na_values.all`` should be implemented
"""
raise AbstractMethodError(self)
def _values_for_argsort(self) -> np.ndarray:
"""
Return values for sorting.
Returns
-------
ndarray
The transformed values should maintain the ordering between values
within the array.
See Also
--------
ExtensionArray.argsort
"""
# Note: this is used in `ExtensionArray.argsort`.
return np.array(self)
def argsort(self, ascending=True, kind="quicksort", *args, **kwargs):
"""
Return the indices that would sort this array.
Parameters
----------
ascending : bool, default True
Whether the indices should result in an ascending
or descending sort.
kind : {'quicksort', 'mergesort', 'heapsort'}, optional
Sorting algorithm.
*args, **kwargs:
passed through to :func:`numpy.argsort`.
Returns
-------
index_array : ndarray
Array of indices that sort ``self``. If NaN values are contained,
NaN values are placed at the end.
See Also
--------
numpy.argsort : Sorting implementation used internally.
"""
# Implementor note: You have two places to override the behavior of
# argsort.
# 1. _values_for_argsort : construct the values passed to np.argsort
# 2. argsort : total control over sorting.
ascending = nv.validate_argsort_with_ascending(ascending, args, kwargs)
result = nargsort(self, kind=kind, ascending=ascending, na_position="last")
return result
def fillna(self, value=None, method=None, limit=None):
"""
Fill NA/NaN values using the specified method.
Parameters
----------
value : scalar, array-like
If a scalar value is passed it is used to fill all missing values.
Alternatively, an array-like 'value' can be given. It's expected
that the array-like have the same length as 'self'.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed Series
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled.
Returns
-------
filled : ExtensionArray with NA/NaN filled
"""
value, method = validate_fillna_kwargs(value, method)
mask = self.isna()
if is_array_like(value):
if len(value) != len(self):
raise ValueError(
"Length of 'value' does not match. Got ({}) "
" expected {}".format(len(value), len(self))
)
value = value[mask]
if mask.any():
if method is not None:
func = pad_1d if method == "pad" else backfill_1d
new_values = func(self.astype(object), limit=limit, mask=mask)
new_values = self._from_sequence(new_values, dtype=self.dtype)
else:
# fill with value
new_values = self.copy()
new_values[mask] = value
else:
new_values = self.copy()
return new_values
def dropna(self):
"""
Return ExtensionArray without NA values.
Returns
-------
valid : ExtensionArray
"""
return self[~self.isna()]
def shift(self, periods: int = 1, fill_value: object = None) -> ABCExtensionArray:
"""
Shift values by desired number.
Newly introduced missing values are filled with
``self.dtype.na_value``.
.. versionadded:: 0.24.0
Parameters
----------
periods : int, default 1
The number of periods to shift. Negative values are allowed
for shifting backwards.
fill_value : object, optional
The scalar value to use for newly introduced missing values.
The default is ``self.dtype.na_value``
.. versionadded:: 0.24.0
Returns
-------
shifted : ExtensionArray
Notes
-----
If ``self`` is empty or ``periods`` is 0, a copy of ``self`` is
returned.
If ``periods > len(self)``, then an array of size
len(self) is returned, with all values filled with
``self.dtype.na_value``.
"""
# Note: this implementation assumes that `self.dtype.na_value` can be
# stored in an instance of your ExtensionArray with `self.dtype`.
if not len(self) or periods == 0:
return self.copy()
if isna(fill_value):
fill_value = self.dtype.na_value
empty = self._from_sequence(
[fill_value] * min(abs(periods), len(self)), dtype=self.dtype
)
if periods > 0:
a = empty
b = self[:-periods]
else:
a = self[abs(periods) :]
b = empty
return self._concat_same_type([a, b])
def unique(self):
"""
Compute the ExtensionArray of unique values.
Returns
-------
uniques : ExtensionArray
"""
uniques = unique(self.astype(object))
return self._from_sequence(uniques, dtype=self.dtype)
def searchsorted(self, value, side="left", sorter=None):
"""
Find indices where elements should be inserted to maintain order.
.. versionadded:: 0.24.0
Find the indices into a sorted array `self` (a) such that, if the
corresponding elements in `value` were inserted before the indices,
the order of `self` would be preserved.
Assuming that `self` is sorted:
====== ================================
`side` returned index `i` satisfies
====== ================================
left ``self[i-1] < value <= self[i]``
right ``self[i-1] <= value < self[i]``
====== ================================
Parameters
----------
value : array_like
Values to insert into `self`.
side : {'left', 'right'}, optional
If 'left', the index of the first suitable location found is given.
If 'right', return the last such index. If there is no suitable
index, return either 0 or N (where N is the length of `self`).
sorter : 1-D array_like, optional
Optional array of integer indices that sort array a into ascending
order. They are typically the result of argsort.
Returns
-------
array of ints
Array of insertion points with the same shape as `value`.
See Also
--------
numpy.searchsorted : Similar method from NumPy.
"""
# Note: the base tests provided by pandas only test the basics.
# We do not test
# 1. Values outside the range of the `data_for_sorting` fixture
# 2. Values between the values in the `data_for_sorting` fixture
# 3. Missing values.
arr = self.astype(object)
return arr.searchsorted(value, side=side, sorter=sorter)
def _values_for_factorize(self) -> Tuple[np.ndarray, Any]:
"""
Return an array and missing value suitable for factorization.
Returns
-------
values : ndarray
An array suitable for factorization. This should maintain order
and be a supported dtype (Float64, Int64, UInt64, String, Object).
By default, the extension array is cast to object dtype.
na_value : object
The value in `values` to consider missing. This will be treated
as NA in the factorization routines, so it will be coded as
`na_sentinal` and not included in `uniques`. By default,
``np.nan`` is used.
Notes
-----
The values returned by this method are also used in
:func:`pandas.util.hash_pandas_object`.
"""
return self.astype(object), np.nan
def factorize(self, na_sentinel: int = -1) -> Tuple[np.ndarray, ABCExtensionArray]:
"""
Encode the extension array as an enumerated type.
Parameters
----------
na_sentinel : int, default -1
Value to use in the `labels` array to indicate missing values.
Returns
-------
labels : ndarray
An integer NumPy array that's an indexer into the original
ExtensionArray.
uniques : ExtensionArray
An ExtensionArray containing the unique values of `self`.
.. note::
uniques will *not* contain an entry for the NA value of
the ExtensionArray if there are any missing values present
in `self`.
See Also
--------
factorize : Top-level factorize method that dispatches here.
Notes
-----
:meth:`pandas.factorize` offers a `sort` keyword as well.
"""
# Implementer note: There are two ways to override the behavior of
# pandas.factorize
# 1. _values_for_factorize and _from_factorize.
# Specify the values passed to pandas' internal factorization
# routines, and how to convert from those values back to the
# original ExtensionArray.
# 2. ExtensionArray.factorize.
# Complete control over factorization.
arr, na_value = self._values_for_factorize()
labels, uniques = _factorize_array(
arr, na_sentinel=na_sentinel, na_value=na_value
)
uniques = self._from_factorized(uniques, self)
return labels, uniques
_extension_array_shared_docs[
"repeat"
] = """
Repeat elements of a %(klass)s.
Returns a new %(klass)s where each element of the current %(klass)s
is repeated consecutively a given number of times.
Parameters
----------
repeats : int or array of ints
The number of repetitions for each element. This should be a
non-negative integer. Repeating 0 times will return an empty
%(klass)s.
axis : None
Must be ``None``. Has no effect but is accepted for compatibility
with numpy.
Returns
-------
repeated_array : %(klass)s
Newly created %(klass)s with repeated elements.
See Also
--------
Series.repeat : Equivalent function for Series.
Index.repeat : Equivalent function for Index.
numpy.repeat : Similar method for :class:`numpy.ndarray`.
ExtensionArray.take : Take arbitrary positions.
Examples
--------
>>> cat = pd.Categorical(['a', 'b', 'c'])
>>> cat
[a, b, c]
Categories (3, object): [a, b, c]
>>> cat.repeat(2)
[a, a, b, b, c, c]
Categories (3, object): [a, b, c]
>>> cat.repeat([1, 2, 3])
[a, b, b, c, c, c]
Categories (3, object): [a, b, c]
"""
@Substitution(klass="ExtensionArray")
@Appender(_extension_array_shared_docs["repeat"])
def repeat(self, repeats, axis=None):
nv.validate_repeat(tuple(), dict(axis=axis))
ind = np.arange(len(self)).repeat(repeats)
return self.take(ind)
# ------------------------------------------------------------------------
# Indexing methods
# ------------------------------------------------------------------------
def take(
self, indices: Sequence[int], allow_fill: bool = False, fill_value: Any = None
) -> ABCExtensionArray:
"""
Take elements from an array.
Parameters
----------
indices : sequence of integers
Indices to be taken.
allow_fill : bool, default False
How to handle negative values in `indices`.
* False: negative values in `indices` indicate positional indices
from the right (the default). This is similar to
:func:`numpy.take`.
* True: negative values in `indices` indicate
missing values. These values are set to `fill_value`. Any other
other negative values raise a ``ValueError``.
fill_value : any, optional
Fill value to use for NA-indices when `allow_fill` is True.
This may be ``None``, in which case the default NA value for
the type, ``self.dtype.na_value``, is used.
For many ExtensionArrays, there will be two representations of
`fill_value`: a user-facing "boxed" scalar, and a low-level
physical NA value. `fill_value` should be the user-facing version,
and the implementation should handle translating that to the
physical version for processing the take if necessary.
Returns
-------
ExtensionArray
Raises
------
IndexError
When the indices are out of bounds for the array.
ValueError
When `indices` contains negative values other than ``-1``
and `allow_fill` is True.
See Also
--------
numpy.take
api.extensions.take
Notes
-----
ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
``iloc``, when `indices` is a sequence of values. Additionally,
it's called by :meth:`Series.reindex`, or any other method
that causes realignment, with a `fill_value`.
Examples
--------
Here's an example implementation, which relies on casting the
extension array to object dtype. This uses the helper method
:func:`pandas.api.extensions.take`.
.. code-block:: python
def take(self, indices, allow_fill=False, fill_value=None):
from pandas.core.algorithms import take
# If the ExtensionArray is backed by an ndarray, then
# just pass that here instead of coercing to object.
data = self.astype(object)
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
# fill value should always be translated from the scalar
# type for the array, to the physical storage type for
# the data, before passing to take.
result = take(data, indices, fill_value=fill_value,
allow_fill=allow_fill)
return self._from_sequence(result, dtype=self.dtype)
"""
# Implementer note: The `fill_value` parameter should be a user-facing
# value, an instance of self.dtype.type. When passed `fill_value=None`,
# the default of `self.dtype.na_value` should be used.
# This may differ from the physical storage type your ExtensionArray
# uses. In this case, your implementation is responsible for casting
# the user-facing type to the storage type, before using
# pandas.api.extensions.take
raise AbstractMethodError(self)
def copy(self) -> ABCExtensionArray:
"""
Return a copy of the array.
Returns
-------
ExtensionArray
"""
raise AbstractMethodError(self)
def view(self, dtype=None) -> Union[ABCExtensionArray, np.ndarray]:
"""
Return a view on the array.
Parameters
----------
dtype : str, np.dtype, or ExtensionDtype, optional
Default None
Returns
-------
ExtensionArray
"""
# NB:
# - This must return a *new* object referencing the same data, not self.
# - The only case that *must* be implemented is with dtype=None,
# giving a view with the same dtype as self.
if dtype is not None:
raise NotImplementedError(dtype)
return self[:]
# ------------------------------------------------------------------------
# Printing
# ------------------------------------------------------------------------
def __repr__(self):
from pandas.io.formats.printing import format_object_summary
template = "{class_name}{data}\nLength: {length}, dtype: {dtype}"
# the short repr has no trailing newline, while the truncated
# repr does. So we include a newline in our template, and strip
# any trailing newlines from format_object_summary
data = format_object_summary(
self, self._formatter(), indent_for_name=False
).rstrip(", \n")
class_name = "<{}>\n".format(self.__class__.__name__)
return template.format(
class_name=class_name, data=data, length=len(self), dtype=self.dtype
)
def _formatter(self, boxed: bool = False) -> Callable[[Any], Optional[str]]:
"""Formatting function for scalar values.
This is used in the default '__repr__'. The returned formatting
function receives instances of your scalar type.
Parameters
----------
boxed : bool, default False
An indicated for whether or not your array is being printed
within a Series, DataFrame, or Index (True), or just by
itself (False). This may be useful if you want scalar values
to appear differently within a Series versus on its own (e.g.
quoted or not).
Returns
-------
Callable[[Any], str]
A callable that gets instances of the scalar type and
returns a string. By default, :func:`repr` is used
when ``boxed=False`` and :func:`str` is used when
``boxed=True``.
"""
if boxed:
return str
return repr
# ------------------------------------------------------------------------
# Reshaping
# ------------------------------------------------------------------------
def ravel(self, order="C") -> ABCExtensionArray:
"""
Return a flattened view on this array.
Parameters
----------
order : {None, 'C', 'F', 'A', 'K'}, default 'C'
Returns
-------
ExtensionArray
Notes
-----
- Because ExtensionArrays are 1D-only, this is a no-op.
- The "order" argument is ignored, is for compatibility with NumPy.
"""
return self
@classmethod
def _concat_same_type(
cls, to_concat: Sequence[ABCExtensionArray]
) -> ABCExtensionArray:
"""
Concatenate multiple array.
Parameters
----------
to_concat : sequence of this type
Returns
-------
ExtensionArray
"""
raise AbstractMethodError(cls)
# The _can_hold_na attribute is set to True so that pandas internals
# will use the ExtensionDtype.na_value as the NA value in operations
# such as take(), reindex(), shift(), etc. In addition, those results
# will then be of the ExtensionArray subclass rather than an array
# of objects
_can_hold_na = True
@property
def _ndarray_values(self) -> np.ndarray:
"""
Internal pandas method for lossy conversion to a NumPy ndarray.
This method is not part of the pandas interface.
The expectation is that this is cheap to compute, and is primarily
used for interacting with our indexers.
Returns
-------
array : ndarray
"""
return np.array(self)
def _reduce(self, name, skipna=True, **kwargs):
"""
Return a scalar result of performing the reduction operation.
Parameters
----------
name : str
Name of the function, supported values are:
{ any, all, min, max, sum, mean, median, prod,
std, var, sem, kurt, skew }.
skipna : bool, default True
If True, skip NaN values.
**kwargs
Additional keyword arguments passed to the reduction function.
Currently, `ddof` is the only supported kwarg.
Returns
-------
scalar
Raises
------
TypeError : subclass does not define reductions
"""
raise TypeError(
"cannot perform {name} with type {dtype}".format(
name=name, dtype=self.dtype
)
)
class ExtensionOpsMixin:
"""
A base class for linking the operators to their dunder names.
.. note::
You may want to set ``__array_priority__`` if you want your
implementation to be called when involved in binary operations
with NumPy arrays.
"""
@classmethod
def _add_arithmetic_ops(cls):
cls.__add__ = cls._create_arithmetic_method(operator.add)
cls.__radd__ = cls._create_arithmetic_method(ops.radd)
cls.__sub__ = cls._create_arithmetic_method(operator.sub)
cls.__rsub__ = cls._create_arithmetic_method(ops.rsub)
cls.__mul__ = cls._create_arithmetic_method(operator.mul)
cls.__rmul__ = cls._create_arithmetic_method(ops.rmul)
cls.__pow__ = cls._create_arithmetic_method(operator.pow)
cls.__rpow__ = cls._create_arithmetic_method(ops.rpow)
cls.__mod__ = cls._create_arithmetic_method(operator.mod)
cls.__rmod__ = cls._create_arithmetic_method(ops.rmod)
cls.__floordiv__ = cls._create_arithmetic_method(operator.floordiv)
cls.__rfloordiv__ = cls._create_arithmetic_method(ops.rfloordiv)
cls.__truediv__ = cls._create_arithmetic_method(operator.truediv)
cls.__rtruediv__ = cls._create_arithmetic_method(ops.rtruediv)
cls.__divmod__ = cls._create_arithmetic_method(divmod)
cls.__rdivmod__ = cls._create_arithmetic_method(ops.rdivmod)
@classmethod
def _add_comparison_ops(cls):
cls.__eq__ = cls._create_comparison_method(operator.eq)
cls.__ne__ = cls._create_comparison_method(operator.ne)
cls.__lt__ = cls._create_comparison_method(operator.lt)
cls.__gt__ = cls._create_comparison_method(operator.gt)
cls.__le__ = cls._create_comparison_method(operator.le)
cls.__ge__ = cls._create_comparison_method(operator.ge)
class ExtensionScalarOpsMixin(ExtensionOpsMixin):
"""
A mixin for defining ops on an ExtensionArray.
It is assumed that the underlying scalar objects have the operators
already defined.
Notes
-----
If you have defined a subclass MyExtensionArray(ExtensionArray), then
use MyExtensionArray(ExtensionArray, ExtensionScalarOpsMixin) to
get the arithmetic operators. After the definition of MyExtensionArray,
insert the lines
MyExtensionArray._add_arithmetic_ops()
MyExtensionArray._add_comparison_ops()
to link the operators to your class.
.. note::
You may want to set ``__array_priority__`` if you want your
implementation to be called when involved in binary operations
with NumPy arrays.
"""
@classmethod
def _create_method(cls, op, coerce_to_dtype=True):
"""
A class method that returns a method that will correspond to an
operator for an ExtensionArray subclass, by dispatching to the
relevant operator defined on the individual elements of the
ExtensionArray.
Parameters
----------
op : function
An operator that takes arguments op(a, b)
coerce_to_dtype : bool, default True
boolean indicating whether to attempt to convert
the result to the underlying ExtensionArray dtype.
If it's not possible to create a new ExtensionArray with the
values, an ndarray is returned instead.
Returns
-------
Callable[[Any, Any], Union[ndarray, ExtensionArray]]
A method that can be bound to a class. When used, the method
receives the two arguments, one of which is the instance of
this class, and should return an ExtensionArray or an ndarray.
Returning an ndarray may be necessary when the result of the
`op` cannot be stored in the ExtensionArray. The dtype of the
ndarray uses NumPy's normal inference rules.
Examples
--------
Given an ExtensionArray subclass called MyExtensionArray, use
>>> __add__ = cls._create_method(operator.add)
in the class definition of MyExtensionArray to create the operator
for addition, that will be based on the operator implementation
of the underlying elements of the ExtensionArray
"""
def _binop(self, other):
def convert_values(param):
if isinstance(param, ExtensionArray) or is_list_like(param):
ovalues = param
else: # Assume its an object
ovalues = [param] * len(self)
return ovalues
if isinstance(other, (ABCSeries, ABCIndexClass)):
# rely on pandas to unbox and dispatch to us
return NotImplemented
lvalues = self
rvalues = convert_values(other)
# If the operator is not defined for the underlying objects,
# a TypeError should be raised
res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
def _maybe_convert(arr):
if coerce_to_dtype:
# https://github.com/pandas-dev/pandas/issues/22850
# We catch all regular exceptions here, and fall back
# to an ndarray.
try:
res = self._from_sequence(arr)
except Exception:
res = np.asarray(arr)
else:
res = np.asarray(arr)
return res
if op.__name__ in {"divmod", "rdivmod"}:
a, b = zip(*res)
res = _maybe_convert(a), _maybe_convert(b)
else:
res = _maybe_convert(res)
return res
op_name = ops._get_op_name(op, True)
return set_function_name(_binop, op_name, cls)
@classmethod
def _create_arithmetic_method(cls, op):
return cls._create_method(op)
@classmethod
def _create_comparison_method(cls, op):
return cls._create_method(op, coerce_to_dtype=False)
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