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B �5�g�� � @ s� U d Z ddlmZ ddlZddlmZmZmZmZm Z m Z m Z ddl Z ddlmZ ddlmZmZmZmZmZ ddlmZ ddlmZ dd lm Z dd l m Z m Z m Z dd l!m"Z"m#Z# dd l$m%Z% dd l&m'Z'm(Z(m)Z)m*Z* ddl+m,Z, ddl-m.Z.m/Z/m0Z0 ddl1m2Z2 ddl3m4Z4m5Z5 ddl6m7Z7m8Z8m9Z9 ddl:m;Z;m<Z< e�r\ddlm=Z= G dd� dd�Z>i Z?de@d< e ddd�ZAG d d� d�ZBG d d � d �ZCG d d � d eC�ZDdS )!z� An interface for extending pandas with custom arrays. .. warning:: This is an experimental API and subject to breaking changes without warning. � )� annotationsN)� TYPE_CHECKING�Any�Callable�Iterator�Sequence�TypeVar�cast)�lib)� ArrayLike�Dtype� FillnaOptions�PositionalIndexer�Shape)�set_function_name)�function)�AbstractMethodError)�Appender� Substitution�cache_readonly)�validate_bool_kwarg�validate_fillna_kwargs)� maybe_cast_to_extension_array)�is_dtype_equal� is_list_like� is_scalar� pandas_dtype)�ExtensionDtype)� ABCDataFrame�ABCIndex� ABCSeries)�isna)�missing�ops)�factorize_array�isin�unique)� nargminmax�nargsort)�Literalc @ s8 e Zd Zdd�ddd�dd�Zdd�ddd�dd�Zd S ) � ExtensionArraySupportsAnyAllT)�skipna�bool)r+ �returnc C s d S )N� )�selfr+ r. r. �M/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/arrays/base.py�anyN s z ExtensionArraySupportsAnyAll.anyc C s d S )Nr. )r/ r+ r. r. r0 �allQ s z ExtensionArraySupportsAnyAll.allN)�__name__� __module__� __qualname__r1 r2 r. r. r. r0 r* M s r* �ExtensionArrayzdict[str, str]� _extension_array_shared_docs�ExtensionArrayT)�boundc @ sP e Zd ZU dZdZeddd�dd�dd ��Zeddd�dd�d d ��Zed d � �Zddd�dd�Z dddd�dd�Z dd�dd �Z d d�d d �Z d d!d�d"d#�Z dd$d%�d&d'�Zdd$d%�d(d)�Zddejfdd*d+d,�d-d.�Zed/d�d0d1��Zed2d�d3d4��Zedd�d5d6��Zedd�d7d8��Zedd�d9d:��Zd�d<d=�Zd>d�d?d@�Zd+d�dAdB�Zd�d*dEdEd+dF�dGdH�Z d�d*ddI�dJdK�Z d�d*ddI�dLdM�Z d�dNdOdPdQ�dRdS�Z dTdU� Z d�dd d dW�dXdY�Z!dZdZd[�d\d]�Z"d�d_d`�Z#d d*d%�dadb�Z$d+d�dcdd�Z%ded�dfdg�Z&d�ddidj�dkdl�Z'dme(dn< e)d do�e*e(dn �d�dpdPdq�drds���Z+dddt�dZdud*ddZdv�dwdx�Z,dZdZd[�dydz�Z-d�dd$d{�d|d}�Z.dEd�d~d�Z/d�d*d�d��d�d��Z0dd d��d�d��Z1ed d�d�d���Z2d�d�d d��d�d��Z3ed�d�dZd��d�d���Z4e5d*d�d�d���Z6d;d��dEd*d��d�d��Z7de8d�< dZdZd[�d�d��Z9ed2d/d��d�d���Z:dS )�r6 a� 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`. Attributes ---------- dtype nbytes ndim shape Methods ------- argsort astype copy dropna factorize fillna equals isin isna ravel repeat searchsorted shift take unique view _concat_same_type _formatter _from_factorized _from_sequence _from_sequence_of_strings _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__ * __eq__ * 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. By default, ExtensionArrays are not hashable. Immutable subclasses may override this behavior. � extensionNF)�dtype�copyz Dtype | None)r; c C s t | ��dS )aN 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`` or be converted into this type in this method. dtype : dtype, optional Construct for this particular dtype. This should be a Dtype compatible with the ExtensionArray. copy : bool, default False If True, copy the underlying data. Returns ------- ExtensionArray N)r )�clsZscalarsr; r< r. r. r0 �_from_sequence� s z ExtensionArray._from_sequencec C s t | ��dS )a Construct a new ExtensionArray from a sequence of strings. 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 : bool, default False If True, copy the underlying data. Returns ------- ExtensionArray N)r )r= �stringsr; r< r. r. r0 �_from_sequence_of_strings� s z(ExtensionArray._from_sequence_of_stringsc C s t | ��dS )a� 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 : Top-level factorize method that dispatches here. ExtensionArray.factorize : Encode the extension array as an enumerated type. N)r )r= �values�originalr. r. r0 �_from_factorized s z ExtensionArray._from_factorizedr zExtensionArray | Any)�itemr- c C s t | ��dS )aE 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. N)r )r/ rD r. r. r0 � __getitem__& s zExtensionArray.__getitem__zint | slice | np.ndarrayr �None)�key�valuer- c C s t t| �� d���dS )a^ 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 z does not implement __setitem__.N)�NotImplementedError�type)r/ rG rH r. r. r0 � __setitem__E s +zExtensionArray.__setitem__�int)r- c C s t | ��dS )z\ Length of this array Returns ------- length : int N)r )r/ r. r. r0 �__len__r s zExtensionArray.__len__z Iterator[Any]c c s$ x t t| ��D ]}| | V qW dS )z5 Iterate over elements of the array. N)�range�len)r/ �ir. r. r0 �__iter__| s zExtensionArray.__iter__�objectzbool | np.bool_c C sV t |�rFt|�rF| jsdS || jjks4t|| jj�r@| �� �� S dS n || k�� S dS )z, Return for `item in self`. FN)r r! � _can_hold_nar; �na_value� isinstancerJ r1 )r/ rD r. r. r0 � __contains__� s zExtensionArray.__contains__r )�otherr- c C s t | ��dS )zE Return for `self == other` (element-wise equality). N)r )r/ rW r. r. r0 �__eq__� s zExtensionArray.__eq__c C s | |k S )zH Return for `self != other` (element-wise in-equality). r. )r/ rW r. r. r0 �__ne__� s zExtensionArray.__ne__r, z np.ndarray)r; r< r- c C s> t j| |d�}|s |tjk r$|�� }|tjk r:||| �� < |S )aj Convert to a NumPy ndarray. .. versionadded:: 1.0.0 This is similar to :meth:`numpy.asarray`, but may provide additional control over how the conversion is done. Parameters ---------- dtype : str or numpy.dtype, optional The dtype to pass to :meth:`numpy.asarray`. copy : bool, default False Whether to ensure that the returned value is a not a view on another array. Note that ``copy=False`` does not *ensure* that ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that a copy is made, even if not strictly necessary. na_value : Any, optional The value to use for missing values. The default value depends on `dtype` and the type of the array. Returns ------- numpy.ndarray )r; )�np�asarrayr � no_defaultr< r! )r/ r; r< rT �resultr. r. r0 �to_numpy� s $ zExtensionArray.to_numpyr c C s t | ��dS )z2 An instance of 'ExtensionDtype'. N)r )r/ r. r. r0 r; � s zExtensionArray.dtyper c C s t | �fS )z9 Return a tuple of the array dimensions. )rO )r/ r. r. r0 �shape� s zExtensionArray.shapec C s t �| j�S )z6 The number of elements in the array. )rZ �prodr_ )r/ r. r. r0 �size� s zExtensionArray.sizec C s dS )zH Extension Arrays are only allowed to be 1-dimensional. � r. )r/ r. r. r0 �ndim� s zExtensionArray.ndimc C s t | ��dS )zL The number of bytes needed to store this object in memory. N)r )r/ r. r. r0 �nbytes� s zExtensionArray.nbytesTc C s\ ddl m} t|�}t|| j�r0|s(| S | �� S t||�rL|�� j| dd�S t j | ||d�S )a� 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. r )� StringDtypeF)r< )r; r< ) Zpandas.core.arrays.string_re r r r; r< rU Zconstruct_array_typer> rZ �array)r/ r; r< re r. r. r0 �astype s zExtensionArray.astypez)np.ndarray | ExtensionArraySupportsAnyAllc C s t | ��dS )a� 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 N)r )r/ r. r. r0 r! ' s zExtensionArray.isnac C s t �| �S )a= Return values for sorting. Returns ------- ndarray The transformed values should maintain the ordering between values within the array. See Also -------- ExtensionArray.argsort : Return the indices that would sort this array. )rZ rf )r/ r. r. r0 �_values_for_argsort= s z"ExtensionArray._values_for_argsort� quicksort�last�str)� ascending�kind� na_positionr- c O s2 t �|||�}| �� }t||||t�| �� �d�S )a� 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', 'stable'}, optional Sorting algorithm. *args, **kwargs: Passed through to :func:`numpy.argsort`. Returns ------- np.ndarray[np.intp] 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. )rm rl rn �mask)�nvZ validate_argsort_with_ascendingrh r( rZ r[ r! )r/ rl rm rn �args�kwargsrA r. r. r0 �argsortN s #zExtensionArray.argsort)r+ r- c C s( t |d� |s | �� �� r t�t| d�S )aq Return the index of minimum value. In case of multiple occurrences of the minimum value, the index corresponding to the first occurrence is returned. Parameters ---------- skipna : bool, default True Returns ------- int See Also -------- ExtensionArray.argmax r+ �argmin)r r! r1 rI r' )r/ r+ r. r. r0 rt | s zExtensionArray.argminc C s( t |d� |s | �� �� r t�t| d�S )aq Return the index of maximum value. In case of multiple occurrences of the maximum value, the index corresponding to the first occurrence is returned. Parameters ---------- skipna : bool, default True Returns ------- int See Also -------- ExtensionArray.argmin r+ �argmax)r r! r1 rI r' )r/ r+ r. r. r0 ru � s zExtensionArray.argmaxzobject | ArrayLike | NonezFillnaOptions | Nonez int | None)rH �method�limitc C s� t ||�\}}| �� }t�||t| ��}|�� r~|dk rlt�|�}|| �t�||d�\}}| j || j d�}q�| � � }|||< n| � � }|S )a� 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 ------- ExtensionArray With NA/NaN filled. N)rw ro )r; ) r r! r" Zcheck_value_sizerO r1 Z get_fill_funcrg rR r> r; r< )r/ rH rv rw ro �func� new_values�_r. r. r0 �fillna� s zExtensionArray.fillnac C s | | � � S )zz Return ExtensionArray without NA values. Returns ------- valid : ExtensionArray )r! )r/ r. r. r0 �dropna� s zExtensionArray.dropnarb )�periods� fill_valuer- c C s� t | �r|dkr| �� S t|�r(| jj}| j|gtt|�t | �� | jd�}|dkrh|}| d| � }n| t|�d� }|}| �||g�S )a> Shift values by desired number. Newly introduced missing values are filled with ``self.dtype.na_value``. 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``. Returns ------- ExtensionArray Shifted. 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``. r )r; N) rO r< r! r; rT r> �min�abs�_concat_same_type)r/ r} r~ �empty�a�br. r. r0 �shift� s ! zExtensionArray.shiftr8 )r/ r- c C s t | �t��}| j|| jd�S )z� Compute the ExtensionArray of unique values. Returns ------- uniques : ExtensionArray )r; )r& rg rR r> r; )r/ �uniquesr. r. r0 r& s zExtensionArray.unique�leftc C s | � t�}|j|||d�S )as Find indices where elements should be inserted to maintain order. 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. )�side�sorter)rg rR � searchsorted)r/ rH r� r� �arrr. r. r0 r� * s + zExtensionArray.searchsortedc C s� t | �t |�krdS tt|�}t| j|j�s0dS t| �t|�krDdS | |k}t|t�r`|�d�}| �� |�� @ }t ||B � � �S dS )a� Return if another array is equivalent to this array. Equivalent means that both arrays have the same shape and dtype, and all values compare equal. Missing values in the same location are considered equal (in contrast with normal equality). Parameters ---------- other : ExtensionArray Array to compare to this Array. Returns ------- boolean Whether the arrays are equivalent. FN) rJ r r6 r r; rO rU r{ r! r, r2 )r/ rW Z equal_valuesZequal_nar. r. r0 �equalsX s zExtensionArray.equalsc C s t t�| �|�S )a Pointwise comparison for set containment in the given values. Roughly equivalent to `np.array([x in values for x in self])` Parameters ---------- values : Sequence Returns ------- np.ndarray[bool] )r% rZ r[ )r/ rA r. r. r0 r% z s zExtensionArray.isinztuple[np.ndarray, Any]c C s | � t�tjfS )a 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_sentinel` 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`. )rg rR rZ �nan)r/ r. r. r0 �_values_for_factorize� s z$ExtensionArray._values_for_factorize���z!tuple[np.ndarray, ExtensionArray])� na_sentinelr- c C s2 | � � \}}t|||d�\}}| �|| �}||fS )ag Encode the extension array as an enumerated type. Parameters ---------- na_sentinel : int, default -1 Value to use in the `codes` array to indicate missing values. Returns ------- codes : ndarray An integer NumPy array that's an indexer into the original ExtensionArray. uniques : ExtensionArray An ExtensionArray containing the unique values of `self`. .. note:: uniques will *not* contain an entry for the NA value of the ExtensionArray if there are any missing values present in `self`. See Also -------- factorize : Top-level factorize method that dispatches here. Notes ----- :meth:`pandas.factorize` offers a `sort` keyword as well. )r� rT )r� r$ rC )r/ r� r� rT �codesr� r. r. r0 � factorize� s ' zExtensionArray.factorizeaL 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'] �repeat)�klasszint | Sequence[int])�repeats�axisc C s. t �dd|i� t�t| ���|�}| �|�S )Nr. r� )rp Zvalidate_repeatrZ �arangerO r� �take)r/ r� r� �indr. r. r0 r� s zExtensionArray.repeat)� allow_fillr~ z Sequence[int])r/ �indicesr� r~ r- c C s t | ��dS )a� Take elements from an array. Parameters ---------- indices : sequence of int 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 : Take elements from an array along an axis. api.extensions.take : Take elements from an array. 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) N)r )r/ r� r� r~ r. r. r0 r� s ]zExtensionArray.takec C s t | ��dS )ze Return a copy of the array. Returns ------- ExtensionArray N)r )r/ r. r. r0 r< j s zExtensionArray.copy)r; r- c C s |dk rt |��| dd� S )a) Return a view on the array. Parameters ---------- dtype : str, np.dtype, or ExtensionDtype, optional Default None. Returns ------- ExtensionArray or np.ndarray A view on the :class:`ExtensionArray`'s data. N)rI )r/ r; r. r. r0 �viewt s zExtensionArray.viewc C sT ddl m} || | �� dd��d�}dt| �j� d�}|� |� dt| �� d | j� �S ) Nr )�format_object_summaryF)Zindent_for_namez, �<z> z Length: z , dtype: )Zpandas.io.formats.printingr� � _formatter�rstriprJ r3 rO r; )r/ r� �data� class_namer. r. r0 �__repr__� s zExtensionArray.__repr__zCallable[[Any], str | None])�boxedr- c C s |rt S tS )aY 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``. )rk �repr)r/ r� r. r. r0 r� � s zExtensionArray._formatter)�axesr- c G s | dd� S )z� Return a transposed view on this array. Because ExtensionArrays are always 1D, this is a no-op. It is included for compatibility with np.ndarray. Nr. )r/ r� r. r. r0 � transpose� s zExtensionArray.transposec C s | � � S )N)r� )r/ r. r. r0 �T� s zExtensionArray.T�Cz$Literal[('C', 'F', 'A', 'K')] | None)�orderr- c C s | S )ax 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. r. )r/ r� r. r. r0 �ravel� s zExtensionArray.ravelztype[ExtensionArrayT]zSequence[ExtensionArrayT])r= � to_concatr- c C s t | ��dS )z� Concatenate multiple array of this dtype. Parameters ---------- to_concat : sequence of this type Returns ------- ExtensionArray N)r )r= r� r. r. r0 r� � s z ExtensionArray._concat_same_typec C s | j jS )N)r; rS )r/ r. r. r0 rS � s zExtensionArray._can_hold_na)r+ )�namer+ c K s t d|� d| j� ���dS )a� 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 zcannot perform z with type N)� TypeErrorr; )r/ r� r+ rr r. r. r0 �_reduce� s zExtensionArray._reduce�__hash__c C s t �t �t| ��|�}| �|�S )N)rZ �deleter� rO r� )r/ �locZindexerr. r. r0 r� s zExtensionArray.delete)r_ r; c C sV | j g |d�}t�t�d�|�}|j|dd�}t|| �rB||jkrRtd|� d���|S )zJ Create an ExtensionArray with the given shape and dtype. )r; r� T)r� z5Default 'empty' implementation is invalid for dtype='�')r> rZ Z broadcast_toZintpr� rU r; rI )r= r_ r; �objZtakerr] r. r. r0 �_empty s zExtensionArray._empty)T)Tri rj )T)T)NNN)rb N)r� N)r� )N)N)F)r� );r3 r4 r5 �__doc__Z_typ� classmethodr> r@ rC rE rK rM rQ rV rX rY r r\ r^ �propertyr; r_ ra rc rd rg r! rh rs rt ru r{ r| r� r& r� r� r% r� r� r7 r r r� r� r< r� r� r� r� r� r� r� r rS r� �__annotations__r� r� r. r. r. r0 r6 Z sx } - + " * 2 2 ."\ Z c @ sX e Zd ZdZedd� �Zedd� �Zedd� �Zedd � �Zed d � �Z ed d � �Z dS )�ExtensionOpsMixinz� 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. c C s t | ��d S )N)r )r= �opr. r. r0 �_create_arithmetic_method: s z+ExtensionOpsMixin._create_arithmetic_methodc C sB t | d| �tj�� t | d| �tj�� t | d| �tj�� t | d| �tj�� t | d| �tj�� t | d| �tj �� t | d| �tj �� t | d| �tj �� t | d | �tj �� t | d | �tj �� t | d | �tj�� t | d | �tj�� t | d | �tj�� t | d| �tj�� t | d| �t�� t | d| �tj�� d S )N�__add__�__radd__�__sub__�__rsub__�__mul__�__rmul__�__pow__�__rpow__�__mod__�__rmod__� __floordiv__� __rfloordiv__� __truediv__� __rtruediv__� __divmod__� __rdivmod__)�setattrr� �operator�addr# Zradd�subZrsub�mulZrmul�powZrpow�modZrmod�floordivZ rfloordiv�truedivZrtruediv�divmod�rdivmod)r= r. r. r0 �_add_arithmetic_ops> s z%ExtensionOpsMixin._add_arithmetic_opsc C s t | ��d S )N)r )r= r� r. r. r0 �_create_comparison_methodQ s z+ExtensionOpsMixin._create_comparison_methodc C s| t | d| �tj�� t | d| �tj�� t | d| �tj�� t | d| �tj�� t | d| �tj�� t | d| �tj�� d S )NrX rY �__lt__�__gt__�__le__�__ge__) r� r� r� �eq�ne�lt�gt�le�ge)r= r. r. r0 �_add_comparison_opsU s z%ExtensionOpsMixin._add_comparison_opsc C s t | ��d S )N)r )r= r� r. r. r0 �_create_logical_method^ s z(ExtensionOpsMixin._create_logical_methodc C s| t | d| �tj�� t | d| �tj�� t | d| �tj�� t | d| �tj�� t | d| �tj�� t | d| �tj �� d S )N�__and__�__rand__�__or__�__ror__�__xor__�__rxor__) r� r� r� �and_r# Zrand_�or_Zror_�xorZrxor)r= r. r. r0 �_add_logical_opsb s z"ExtensionOpsMixin._add_logical_opsN) r3 r4 r5 r� r� r� r� r� r� r� r� r. r. r. r0 r� / s r� c @ s6 e Zd ZdZed dd��Zedd� �Zedd � �ZdS ) �ExtensionScalarOpsMixina� 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. TNc s* � ��fdd�}d�j � d�}t||| �S )a 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 c s� � fdd�}t |tttf�r tS � }||�}�fdd�t||�D �}��� fdd�}�jdkrzt|� \}}||�||�fS ||�S )Nc s* t | t�st| �r| }n| gt� � }|S )N)rU r6 r rO )�paramZovalues)r/ r. r0 �convert_values� s zNExtensionScalarOpsMixin._create_method.<locals>._binop.<locals>.convert_valuesc s g | ]\}}� ||��qS r. r. )�.0r� r� )r� r. r0 � <listcomp>� s zJExtensionScalarOpsMixin._create_method.<locals>._binop.<locals>.<listcomp>c s> � r,t t��| �}t|t���s:t�| �}ntj| �d�}|S )N)r; )r rJ rU rZ r[ )r� �res)�coerce_to_dtype� result_dtyper/ r. r0 �_maybe_convert� s zNExtensionScalarOpsMixin._create_method.<locals>._binop.<locals>._maybe_convert> r� r� )rU r r r �NotImplemented�zipr3 ) r/ rW r� ZlvaluesZrvaluesr� r� r� r� )r� r� r� )r/ r0 �_binop� s z6ExtensionScalarOpsMixin._create_method.<locals>._binop�__)r3 r )r= r� r� r� r� Zop_namer. )r� r� r� r0 �_create_method� s (&z&ExtensionScalarOpsMixin._create_methodc C s | � |�S )N)r� )r= r� r. r. r0 r� � s z1ExtensionScalarOpsMixin._create_arithmetic_methodc C s | j |dtd�S )NF)r� r� )r� r, )r= r� r. r. r0 r� � s z1ExtensionScalarOpsMixin._create_comparison_method)TN)r3 r4 r5 r� r� r� r� r� r. r. r. r0 r� l s P r� )Er� � __future__r r� �typingr r r r r r r �numpyrZ Z pandas._libsr Zpandas._typingr r r r r Z pandas.compatr Zpandas.compat.numpyr rp Z pandas.errorsr Zpandas.util._decoratorsr r r Zpandas.util._validatorsr r Zpandas.core.dtypes.castr Zpandas.core.dtypes.commonr r r r Zpandas.core.dtypes.dtypesr Zpandas.core.dtypes.genericr r r Zpandas.core.dtypes.missingr! 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