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B �5�g�@ � @ sp d dl mZ d dlmZmZmZmZ d dlZd dl m Z m Z d dl mZmZmZmZmZmZ d dlmZ d dlmZmZ d dlmZ d d lm Z d d l m Z m Z m Z m!Z!m"Z"m#Z# d d l$m%Z% d d l&m'Z'm(Z( d d l)m Z m*Z* d dl+m,Z,m-Z-m.Z. d dl/m0Z0 d dl1m2Z2 d dl3m4Z4 d dl5m6Z6 e�r>d dl7m8Z8 d dl3m9Z9 eddd�Z:G dd� de �Z;G dd� de2e4�Z<dS )� )� annotations)� TYPE_CHECKING�Any�Sequence�TypeVarN)�lib�missing)� ArrayLike�Dtype�NpDtype�PositionalIndexer�Scalar�type_t)�AbstractMethodError)�cache_readonly�doc)�validate_fillna_kwargs)�ExtensionDtype)�is_dtype_equal� is_integer�is_object_dtype� is_scalar�is_string_dtype� pandas_dtype)� is_array_like)�isna�notna)r �nanops)�factorize_array�isin�take)�masked_reductions)�OpsMixin)�ExtensionArray)�check_array_indexer)�Series)� BooleanArray�BaseMaskedArrayT�BaseMaskedArray)�boundc @ st e Zd ZU dZded< dZded< ejZe dd�dd ��Z e dd�d d ��Z e d d�d d��Z e dd�dd��ZdS )�BaseMaskedDtypez@ Base class for dtypes for BasedMaskedArray subclasses. �str�nameN�typeznp.dtype)�returnc C s t �| j�S )z%Return an instance of our numpy dtype)�np�dtyper- )�self� r2 �O/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/arrays/masked.py� numpy_dtypeO s zBaseMaskedDtype.numpy_dtypec C s | j jS )N)r4 �kind)r1 r2 r2 r3 r5 T s zBaseMaskedDtype.kind�intc C s | j jS )z(Return the number of bytes in this dtype)r4 �itemsize)r1 r2 r2 r3 r7 X s zBaseMaskedDtype.itemsizeztype_t[BaseMaskedArray]c C s t �dS )zq Return the array type associated with this dtype. Returns ------- type N)�NotImplementedError)�clsr2 r2 r3 �construct_array_type] s z$BaseMaskedDtype.construct_array_type)�__name__� __module__� __qualname__�__doc__�__annotations__�base� libmissing�NA�na_valuer r4 r5 r7 � classmethodr: r2 r2 r2 r3 r* D s r* c @ s� e Zd ZU dZded< dZdddd�dd �Zed d �d d ��Zddd�dd�Ze e j �d[ddd�dd��Z dd �dd�Z dd �d d �Z d d � Zd d �d!d"�Zddd�d#d$�Zddejfd%dddd&�d'd(�Zd\d*dd+d,�d-d.�Zd/Zd]d%dd0�d1d2�Zd^d3d4�Zedd �d5d6��Zdd �d7d8�Zed9d:� �Zed d �d;d<��Ze d=d>dd?�d@dA��Z dddB�dddCddD�dEdF�Z dGd �dHdI�Z ddd�dJdK�Z e e j!�d_d dMdN�dOdP��Z!d`ddQdR�dSdT�Z"d)dU�dVddW�dXdY�Z#dS )ar( zf Base class for masked arrays (which use _data and _mask to store the data). numpy based r �_internal_fill_valueFz np.ndarray�bool)�values�mask�copyc C sh t |tj�r|jtjks td��|jdkr2td��|jdkrDtd��|rX|�� }|�� }|| _ || _ d S )NzGmask should be boolean numpy array. Use the 'pd.array' function instead� zvalues must be a 1D arrayzmask must be a 1D array) � isinstancer/ Zndarrayr0 �bool_� TypeError�ndim� ValueErrorrI �_data�_mask)r1 rG rH rI r2 r2 r3 �__init__s s zBaseMaskedArray.__init__r* )r. c C s t | ��d S )N)r )r1 r2 r2 r3 r0 � s zBaseMaskedArray.dtyper zBaseMaskedArray | Any)�itemr. c C sH t |�r$| j| r| jjS | j| S t| |�}t| �| j| | j| �S )N)r rQ r0 rC rP r$ r- )r1 rS r2 r2 r3 � __getitem__� s zBaseMaskedArray.__getitem__Nr' )r1 r. c C s� t ||�\}}| j}t|�rPt|�t| �krHtdt|�� dt| �� ���|| }|�� r�|d k r�t�|�}|| j� � ||� � d�\}}t | �||� t j ��S | � � }|||< n| � � }|S )Nz'Length of 'value' does not match. Got (z ) expected )�limitrH )r rQ r �lenrO �anyr Z get_fill_funcrP rI r- �viewr/ rL )r1 �value�methodrU rH �func� new_valuesZnew_maskr2 r2 r3 �fillna� s& zBaseMaskedArray.fillnaz tuple[np.ndarray, np.ndarray]c C s t | ��d S )N)r )r1 rG r2 r2 r3 �_coerce_to_array� s z BaseMaskedArray._coerce_to_array�Nonec C sV t |�}|r|g}| �|�\}}|r4|d }|d }t| |�}|| j|< || j|< d S )Nr )r r^ r$ rP rQ )r1 �keyrY Z _is_scalarrH r2 r2 r3 � __setitem__� s zBaseMaskedArray.__setitem__c c s< x6t t| ��D ]&}| j| r(| jjV q| j| V qW d S )N)�rangerV rQ r0 rC rP )r1 �ir2 r2 r3 �__iter__� s zBaseMaskedArray.__iter__r6 c C s t | j�S )N)rV rP )r1 r2 r2 r3 �__len__� s zBaseMaskedArray.__len__c C s t | �| j | j�� �S )N)r- rP rQ rI )r1 r2 r2 r3 � __invert__� s zBaseMaskedArray.__invert__zNpDtype | None)r0 rI rC r. c C sx |t jkrtj}|dkr t}| jrdt|�sLt|�sL|tjkrLtd|� d���| j � |�}||| j < n| j j ||d�}|S )aF Convert to a NumPy Array. By default converts to an object-dtype NumPy array. Specify the `dtype` and `na_value` keywords to customize the conversion. Parameters ---------- dtype : dtype, default object The numpy dtype to convert to. copy : bool, default False Whether to ensure that the returned value is a not a view on the 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. This is typically only possible when no missing values are present and `dtype` is the equivalent numpy dtype. na_value : scalar, optional Scalar missing value indicator to use in numpy array. Defaults to the native missing value indicator of this array (pd.NA). Returns ------- numpy.ndarray Examples -------- An object-dtype is the default result >>> a = pd.array([True, False, pd.NA], dtype="boolean") >>> a.to_numpy() array([True, False, <NA>], dtype=object) When no missing values are present, an equivalent dtype can be used. >>> pd.array([True, False], dtype="boolean").to_numpy(dtype="bool") array([ True, False]) >>> pd.array([1, 2], dtype="Int64").to_numpy("int64") array([1, 2]) However, requesting such dtype will raise a ValueError if missing values are present and the default missing value :attr:`NA` is used. >>> a = pd.array([True, False, pd.NA], dtype="boolean") >>> a <BooleanArray> [True, False, <NA>] Length: 3, dtype: boolean >>> a.to_numpy(dtype="bool") Traceback (most recent call last): ... ValueError: cannot convert to bool numpy array in presence of missing values Specify a valid `na_value` instead >>> a.to_numpy(dtype="bool", na_value=False) array([ True, False, False]) Nzcannot convert to 'zZ'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.)rI ) r � no_defaultrA rB �object�_hasnar r rO rP �astyperQ )r1 r0 rI rC �datar2 r2 r3 �to_numpy� s B zBaseMaskedArray.to_numpyTr r )r0 rI r. c C s� t |�}t|| j�r$|r | �� S | S t|t�rp| jj|j|d�}|| jkrP| j n| j �� }|� � }|||dd�S t|t �r�|� � }|j | ||d�S t d��d S )N)rI F)r0 rI z*subclass must implement astype to np.dtype)r r r0 rI rK r* rP rj r4 rQ r: r Z_from_sequencer8 )r1 r0 rI rk rH r9 Zeaclsr2 r2 r3 rj 0 s zBaseMaskedArray.astypei� )r0 r. c C s | j |d�S )z| the array interface, return my values We return an object array here to preserve our scalar values )r0 )rl )r1 r0 r2 r2 r3 � __array__J s zBaseMaskedArray.__array__c C s ddl }|j| j| j|d�S )z6 Convert myself into a pyarrow Array. r N)rH r- )Zpyarrow�arrayrP rQ )r1 r- �par2 r2 r3 �__arrow_array__Q s z BaseMaskedArray.__arrow_array__c C s | j �� S )N)rQ rW )r1 r2 r2 r3 ri Y s zBaseMaskedArray._hasnac C s | j �� S )N)rQ rI )r1 r2 r2 r3 r b s zBaseMaskedArray.isnac C s | j jS )N)r0 rC )r1 r2 r2 r3 � _na_valuee s zBaseMaskedArray._na_valuec C s | j j| jj S )N)rP �nbytesrQ )r1 r2 r2 r3 rr i s zBaseMaskedArray.nbytesztype[BaseMaskedArrayT]zSequence[BaseMaskedArrayT])r9 � to_concatr. c C s2 t �dd� |D ��}t �dd� |D ��}| ||�S )Nc S s g | ] }|j �qS r2 )rP )�.0�xr2 r2 r3 � <listcomp>q s z5BaseMaskedArray._concat_same_type.<locals>.<listcomp>c S s g | ] }|j �qS r2 )rQ )rt ru r2 r2 r3 rv r s )r/ � concatenate)r9 rs rk rH r2 r2 r3 �_concat_same_typem s z!BaseMaskedArray._concat_same_type)� allow_fill� fill_valuez Scalar | None)r1 ry rz r. c C sr t |�r| jn|}t| j|||d�}t| j|d|d�}|r`t|�r`t�|�dk}|||< ||A }t| �||dd�S )N)rz ry T���F)rI ) r rE r rP rQ r r/ �asarrayr- )r1 Zindexerry rz Zdata_fill_value�resultrH Z fill_maskr2 r2 r3 r u s zBaseMaskedArray.taker& c sr ddl m} t�|�}t� j|�}� jrRt|j�oFt � fdd�|D ��}||� j < tj � jj t d�}|||dd�S )Nr )r& c 3 s | ]}|� j jkV qd S )N)r0 rC )rt �val)r1 r2 r3 � <genexpr>� s z'BaseMaskedArray.isin.<locals>.<genexpr>)r0 F)rI )�pandas.core.arraysr& r/ r| r rP ri r r0 rW rQ �zeros�shaperF )r1 rG r& Z values_arrr} Zvalues_have_NArH r2 )r1 r3 r � s zBaseMaskedArray.isinc C s0 | j | j }}|�� }|�� }t| �||dd�S )NF)rI )rP rQ rI r- )r1 rk rH r2 r2 r3 rI � s zBaseMaskedArray.copyr{ z!tuple[np.ndarray, ExtensionArray])� na_sentinelr. c C sT | j }| j}t|||d�\}}|j| jjdd�}t| �|tjt |�t d��}||fS )N)r� rH F)rI )r0 ) rP rQ r rj r0 r4 r- r/ r� rV rF )r1 r� ZarrrH �codesZuniquesr2 r2 r3 � factorize� s zBaseMaskedArray.factorizer% )�dropnar. c C s� ddl m}m} ddlm} | j| j }||��� }|jj � t �}|rP|j }nVt j t|�d dd�}||dd�< | j�� |d< |t �|t j| jjgt d�g�t d�}t jt|�d d�} ||| �}|||d �S ) aA Returns a Series containing counts of each unique value. Parameters ---------- dropna : bool, default True Don't include counts of missing values. Returns ------- counts : Series See Also -------- Series.value_counts r )�Indexr% )� IntegerArrayrJ Zint64)r0 Nr{ rF )�index)�pandasr� r% Z pandas.arraysr� rP rQ � value_countsr� Z_valuesrj rh r/ �emptyrV �sumrw rn r0 rC r� ) r1 r� r� r% r� rk r� r� �countsrH r2 r2 r3 r� � s z BaseMaskedArray.value_counts)�skipnar+ )r, r� c K s� | j }| j}|dkr4tt|�}|||fd|i|��S | jrJ| jdtjd�}ttd| �}||fd||d�|��}t� |�r�t j S |S )N> r� �mean�min�max�prodr� Zfloat64)rC �nanr )Zaxisr� rH ) rP rQ �getattrr! ri rl r/ r� r �isnanrA rB )r1 r, r� �kwargsrk rH �opr} r2 r2 r3 �_reduce� s zBaseMaskedArray._reduce)F)NNN)T)N)N)r{ )T)$r; r< r= r> r? rR �propertyr0 rT r r# r] r^ ra rd re rf r rg rl rj Z__array_priority__rm rp ri r rq rr rD rx r r rI r� r� r� r2 r2 r2 r3 r( i s@ V 2)=� __future__r �typingr r r r �numpyr/ Z pandas._libsr r rA Zpandas._typingr r r r r r Z pandas.errorsr Zpandas.util._decoratorsr r Zpandas.util._validatorsr Zpandas.core.dtypes.baser Zpandas.core.dtypes.commonr r r r r r Z pandas.core.dtypes.inferencer Zpandas.core.dtypes.missingr r Z pandas.corer Zpandas.core.algorithmsr r r Zpandas.core.array_algosr! Zpandas.core.arrayliker" r� r# Zpandas.core.indexersr$ r� r% r& r' r* r( r2 r2 r2 r3 �<module> s. %
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