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B �5�g(� � @ sX d Z ddlmZ ddlmZ ddlZddlZddlmZm Z m Z m Z ddl Z ddl ZddlmZ ddlm mZ ddlmZmZmZ ddlmZ dd lmZm Z m Z dd l m Z dd l!m"Z" dd l#m$Z$m%Z%m&Z&m'Z' dd l(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2 ddl3m4Z4m5Z5 ddl6m7Z7m8Z8m9Z9 ddl:m; m<Z= ddl>m?Z? ddl@mAZA ddlBmCZC ddlDmEZE ddlFm; mGZH ddlImJZJmKZK ddlLmMZM ddlNmOZO ddlPmQZQ ddlRm; mSZS ddlTmU mV mWZW e ddd�ZXddiZYdd d �d d �ZZddd d!d"d#�d$d%�Z[d2d&d'�d(d)�Z\G d*d� de?eEeA�Z]d3d d,d-�d.d/�Z^d0d1� Z_dS )4z SparseArray data structure � )� annotations)�abcN)�Any�Callable�Sequence�TypeVar)�lib)� BlockIndex�IntIndex� SparseIndex)�NaT)�Dtype�NpDtype�Scalar)�function)�PerformanceWarning)�astype_nansafe�"construct_1d_arraylike_from_scalar�find_common_type�maybe_box_datetimelike) � is_array_like� is_bool_dtype�is_datetime64_any_dtype�is_datetime64tz_dtype�is_dtype_equal� is_integer�is_object_dtype� is_scalar�is_string_dtype� pandas_dtype)�ABCIndex� ABCSeries)�isna�na_value_for_dtype�notna)�OpsMixin)�ExtensionArray)� SparseDtype)� PandasObject)� extract_array�sanitize_array)�check_array_indexer)�interpolate_2d)�check_below_min_count� SparseArrayT� SparseArray)�bound�klassz np.ndarray)�arr�returnc C s6 yt j| j| jjd�S tk r0 t �| j�S X dS )a] Create a 0-dim ndarray containing the fill value Parameters ---------- arr : SparseArray Returns ------- fill_value : ndarray 0-dim ndarray with just the fill value. Notes ----- coerce fill_value to arr dtype if possible int64 SparseArray can have NaN as fill_value if there is no missing )�dtypeN)�np�asarray� fill_valuer4 �subtype� ValueError)r2 � r: �U/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/arrays/sparse/array.py� _get_fillX s r< r �strr )�left�right�op�namer3 c C s |� d�r|dd� }| jj}|jj}t||�spt||g�}t|| j�}t||j�}| �|�} |�|�}|j}n|}d}| jj dks�|jj dkr�t j dd��* || � � |� � �} |t | �t |��} W dQ R X | jj dkr�| j} n|j} �n| j�|j��r:t j dd��& || j|j�} |t | �t |��} W dQ R X | j} n�|d dk�r^|| } }|d d� }|d k�r�|d k�r�d |� d �} | j�t j�} |j�t j�}t}n d |� d|� �} | j} |j}tt| �}t j dd��& || | j| j||j|j�\} } } W dQ R X |dk�r| j}t|| | | |d�S )a4 Perform a binary operation between two arrays. Parameters ---------- left : Union[SparseArray, ndarray] right : Union[SparseArray, ndarray] op : Callable The binary operation to perform name str Name of the callable. Returns ------- SparseArray �__� ���Nr �ignore)�all�r� )�and�or�xor�boolZsparse_Z_uint8�_)r4 )� startswithr4 r8 r r r' r7 �astype�sp_index�ngapsr5 �errstate�to_denser< �equals� sp_values�viewZuint8rL �getattr�splib� _wrap_result)r> r? r@ rA ZltypeZrtyper8 r4 Z result_dtype�result�fill�index�opnameZleft_sp_valuesZright_sp_valuesZ sparse_opr: r: r; �_sparse_array_opp s` r^ z Dtype | None)r4 c C sL | � d�r| dd� } | dkr"t}t�|�}t|�r<t|�}t||||d�S )z. wrap op result to have correct dtype rB rC rD )�eq�ne�lt�gt�le�ge)� sparse_indexr7 r4 )rN rL r Zitem_from_zerodimr r/ )rA �datare r7 r4 r: r: r; rY � s rY c @ sZ e Zd ZU dZdZejedg�B Zded< d�d d �d d �Z e d ddddd�dd��Z e dd� �Z d�ddd�dd�Z dd� Ze ddd �d d �d d ��Ze d d � �Zedd!�d"d#��Zedd!�d$d%��Zedd!�d&d'��Zed(d)� �Zejd*d)� �Zed+d!�d,d-��Zedd!�d.d/��Zd0d!�d1d2�Zed3d4� �Zd5d6� Zed0d!�d7d8��Z ed9d!�d:d;��Z ed0d!�d<d=��Z d>d?� Z d�d@dA�Z d�dCdD�Z!dEdF� Z"dGdH� Z#dIdJ� Z$d�dLdM�Z%d�dOdP�dQdR�Z&dSdT� Z'dUdV� Z(dddW�d d!�dXdY�Z)d�dd!�dZd[�Z*d\d!�d]d^�Z+d�d`da�Z,dddb�dcdd�Z-e d deddf�dgdh��Z.d�d d �didj�Z/dkdl� Z0dmdn� Z1e1Z2dodp� Z3dqdr� Z4dNds�d+dOdt�dudv�Z5d�dwdx�Z6d�dzd{�Z7d�d0d0d|d}�d~d�Z8d�d�d��Z9d�d�d��Z:d�d�d��Z;d�d�d��Z<e=j>e?j@fZAd�d+d��d�d��ZBd�d�� ZCd�d�� ZDd d!�d�d��ZEeEZFd d!�d�d��ZGd d!�d�d��ZHd d!�d�d��ZId d!�d�d��ZJd+d!�d�d��ZKd�d�d��ZLdS )�r/ a� An ExtensionArray for storing sparse data. Parameters ---------- data : array-like A dense array of values to store in the SparseArray. This may contain `fill_value`. sparse_index : SparseIndex, optional index : Index fill_value : scalar, optional Elements in `data` that are `fill_value` are not stored in the SparseArray. For memory savings, this should be the most common value in `data`. By default, `fill_value` depends on the dtype of `data`: =========== ========== data.dtype na_value =========== ========== float ``np.nan`` int ``0`` bool False datetime64 ``pd.NaT`` timedelta64 ``pd.NaT`` =========== ========== The fill value is potentially specified in three ways. In order of precedence, these are 1. The `fill_value` argument 2. ``dtype.fill_value`` if `fill_value` is None and `dtype` is a ``SparseDtype`` 3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype` is not a ``SparseDtype`` and `data` is a ``SparseArray``. kind : {'integer', 'block'}, default 'integer' The type of storage for sparse locations. * 'block': Stores a `block` and `block_length` for each contiguous *span* of sparse values. This is best when sparse data tends to be clumped together, with large regions of ``fill-value`` values between sparse values. * 'integer': uses an integer to store the location of each sparse value. dtype : np.dtype or SparseDtype, optional The dtype to use for the SparseArray. For numpy dtypes, this determines the dtype of ``self.sp_values``. For SparseDtype, this determines ``self.sp_values`` and ``self.fill_value``. copy : bool, default False Whether to explicitly copy the incoming `data` array. Attributes ---------- None Methods ------- None Examples -------- >>> from pandas.arrays import SparseArray >>> arr = SparseArray([0, 0, 1, 2]) >>> arr [0, 0, 1, 2] Fill: 0 IntIndex Indices: array([2, 3], dtype=int32) � sparse_arrayZ get_valuesr � _sparse_indexN�integerFz Dtype | None)r4 c C s� |d krt |t�r|j}t |t| ��rV|d kr4|j}|d krB|j}|d krP|j}|j}t |t�r�yt�|�}W n t k r� t |�}Y nX t |t�r�|d kr�|j}|j }|d k r�t |�s�t d��t |��r |d k r�|d kr�tj}|d k r�t|�}n|d k�rd}n|j}t||d d�}|j}|d k �r0t |�}|d k�rHtjg |d�}t|��s�yt|d d�}W n< tk �r� |d k�r�t}t�tj||d��}n� Y nX |�r�|�� }|d k�r�|d k�r�|jn|} | d k�r�tj}nt| �}t |t| ���r|d k�r|j }tj|j|d�} n�|d k�r�t |dd�}t |tj ��s�t |j��r�t j!d|j� d�t"d d � tj|d d�}|t#k�r�t�$d d �}t�|�}t%||||d�\} }}n2tj||d�} t| �|j&k�r�t'dt| �� d���|| _ | | _(t| j|�| _)d S )Nz$must only pass scalars with an indexrH )r4 )r\ T)Z extract_numpyzCreating SparseArray from z_ data loses timezone information. Cast to object before sparse to retain timezone information.rC )� stacklevelzdatetime64[ns]r �ns)�kindr7 r4 zNon array-like type z' must have the same length as the index)*� isinstancer' r7 �typerP r4 rU r= Zconstruct_from_string� TypeErrorr r8 r � Exceptionr5 �nan�len�lengthr �arrayr r* r9 �objectZ atleast_1dr6 �copyr# rh r) �ndarrayr �warnings�warn� UserWarningr � datetime64� make_sparse�npoints�AssertionError�_sparse_values�_dtype) �selfrf re r\ r7 rl r4 rv r} Zfill_value_dtype� sparse_valuesr: r: r; �__init__* s� zSparseArray.__init__ztype[SparseArrayT]z np.ndarrayr' r. )�clsrg re r4 r3 c C s t �| �}||_||_||_|S )N)ru �__new__rh r r� )r� rg re r4 �newr: r: r; � _simple_new� s zSparseArray._simple_newc C sv |j \}}|dkr"td|� d���|�� }|�� |j}|j}tjd|jd�� � }t |j|�}t ||�}| � |||�S )a� Create a SparseArray from a scipy.sparse matrix. .. versionadded:: 0.25.0 Parameters ---------- data : scipy.sparse.sp_matrix This should be a SciPy sparse matrix where the size of the second dimension is 1. In other words, a sparse matrix with a single column. Returns ------- SparseArray Examples -------- >>> import scipy.sparse >>> mat = scipy.sparse.coo_matrix((4, 1)) >>> pd.arrays.SparseArray.from_spmatrix(mat) [0.0, 0.0, 0.0, 0.0] Fill: 0.0 IntIndex Indices: array([], dtype=int32) rH z''data' must have a single column, not '�'r )r4 ) �shaper9 ZtocscZ sort_indicesrf �indicesr5 rt r4 �itemr' r r� ) r� rf rs Zncolr2 �idx�zeror4 r\ r: r: r; � from_spmatrix� s zSparseArray.from_spmatrixzNpDtype | None)r4 r3 c C s� | j }| jjdkr| jS |d krpt| jj�r>|tkr>t�d�}yt� | jjt |��}W n t k rn t }Y nX tj | j||d�}| j|| j�� j< |S )Nr r )r4 )r7 rP rQ rU r r4 r r5 r{ � result_typern ro ru �fullr� � to_int_indexr� )r� r4 r7 �outr: r: r; � __array__� s zSparseArray.__array__c C s d}t |��d S )Nz8SparseArray does not support item assignment via setitem)ro )r� �key�value�msgr: r: r; � __setitem__ s zSparseArray.__setitem__)r4 rv c C s | ||d�S )N)r4 r: )r� Zscalarsr4 rv r: r: r; �_from_sequence s zSparseArray._from_sequencec C s | ||j d�S )N)r4 )r4 )r� �values�originalr: r: r; �_from_factorized s z SparseArray._from_factorized)r3 c C s | j S )zX The SparseIndex containing the location of non- ``fill_value`` points. )rh )r� r: r: r; rP $ s zSparseArray.sp_indexc C s | j S )z� An ndarray containing the non- ``fill_value`` values. Examples -------- >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0) >>> s.sp_values array([1, 2]) )r )r� r: r: r; rU + s zSparseArray.sp_valuesc C s | j S )N)r� )r� r: r: r; r4 8 s zSparseArray.dtypec C s | j jS )z� Elements in `data` that are `fill_value` are not stored. For memory savings, this should be the most common value in the array. )r4 r7 )r� r: r: r; r7 < s zSparseArray.fill_valuec C s t | jj|�| _d S )N)r' r4 r8 r� )r� r� r: r: r; r7 E s r= c C s t | jt�rdS dS dS )zW The kind of sparse index for this array. One of {'integer', 'block'}. ri �blockN)rm rP r )r� r: r: r; rl I s zSparseArray.kindc C s | j }t|�}|| S )N)rU r$ )r� Zsp_vals�maskr: r: r; �_valid_sp_valuesS s z SparseArray._valid_sp_values�intc C s | j jS )N)rP rs )r� r: r: r; �__len__Y s zSparseArray.__len__c C s | j jS )N)r� Z_is_na_fill_value)r� r: r: r; �_null_fill_value\ s z SparseArray._null_fill_valuec C s | j rt|�S | j|kS d S )N)r� r" r7 )r� r7 r: r: r; �_fill_value_matches` s z SparseArray._fill_value_matchesc C s | j j| jj S )N)rU �nbytesrP )r� r: r: r; r� f s zSparseArray.nbytes�floatc C s | j j| j j S )z� The percent of non- ``fill_value`` points, as decimal. Examples -------- >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0) >>> s.density 0.6 )rP r} rs )r� r: r: r; �densityj s zSparseArray.densityc C s | j jS )z� The number of non- ``fill_value`` points. Examples -------- >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0) >>> s.npoints 3 )rP r} )r� r: r: r; r} w s zSparseArray.npointsc C s& t t| j�}t| ��t| j�| j|�S )N)r' rL r� rn r� r" rU rP )r� r4 r: r: r; r" � s zSparseArray.isnac C s� |dkr|dks |dk r*|dk r*t d��nr|dk rhd}t�|t� tt�| �||d�}t| �|| jd�S t� t | j �|| j �}| j r�t | jj|d�}n| j}| �|| j|�S )a� Fill missing values with `value`. Parameters ---------- value : scalar, optional method : str, optional .. warning:: Using 'method' will result in high memory use, as all `fill_value` methods will be converted to an in-memory ndarray limit : int, optional Returns ------- SparseArray Notes ----- When `value` is specified, the result's ``fill_value`` depends on ``self.fill_value``. The goal is to maintain low-memory use. If ``self.fill_value`` is NA, the result dtype will be ``SparseDtype(self.dtype, fill_value=value)``. This will preserve amount of memory used before and after filling. When ``self.fill_value`` is not NA, the result dtype will be ``self.dtype``. Again, this preserves the amount of memory used. Nz(Must specify one of 'method' or 'value'.z0fillna with 'method' requires high memory usage.)�method�limit)r7 )r9 rx ry r r, r5 r6 rn r7 �wherer" rU r� r' r4 r8 r� rh )r� r� r� r� r� Zfilled� new_valuesZ new_dtyper: r: r; �fillna� s ! zSparseArray.fillnarH c C s� t | �r|dkr| �� S t|�r(| jj}t�|| jj�}|| jjkrX| �t || j ��}n| }| j |gt t |�t | �� |jd�}|dkr�|}|d | � }n|t |�d � }|}|�||g�S )Nr )r4 )rr rv r" r4 �na_valuer5 r� r8 rO r' r7 r� �min�abs�_concat_same_type)r� Zperiodsr7 r8 r2 �empty�a�br: r: r; �shift� s zSparseArray.shiftc C sl t | �dks | jjt | �kr dS | j�� j}t |�r@|d dkrDdS |dd� |dd� }t�|d�d S )zk Get the location of the first missing value. Returns ------- int r ���rH NrC )rr rP r} r� r� r5 � searchsorted)r� r� �diffr: r: r; �_first_fill_value_loc� s z!SparseArray._first_fill_value_locc C sB t t�| j��}| �� }|dkr.|�|| j� t| �j|| j d�S )Nr )r4 ) �list�algos�uniquerU r� �insertr7 rn r� r4 )r� �uniquesZfill_locr: r: r; r� � s zSparseArray.uniquec C s t �| �| jfS )N)r5 r6 r7 )r� r: r: r; �_values_for_factorize� s z!SparseArray._values_for_factorizer� c C s. t jt�| �|d�\}}t|| jd�}||fS )N)� na_sentinel)r4 )r� � factorizer5 r6 r/ r4 )r� r� �codesr� r: r: r; r� � s zSparseArray.factorizeTrL )�dropnac C s� ddl m}m} tj| j|d�\}}| jj}|dkr�| jr>|s�| jrLt |�n|| j k}|� � rp|| |7 < n t � |d| j �}t � |d|�}t|t�s�||�}|||d�S )a Returns a Series containing counts of unique values. Parameters ---------- dropna : bool, default True Don't include counts of NaN, even if NaN is in sp_values. Returns ------- counts : Series r )�Index�Series)r� )r\ )Zpandasr� r� r� Zvalue_counts_arraylikerU rP rQ r� r" r7 �anyr5 r� rm r )r� r� r� r� �keys�countsZfcountsr� r: r: r; � value_counts s zSparseArray.value_countsc C s` t |t�rdt|�dkrH|d tkr0|dd � }n|d tkrH|d d� }t|�dkr\td��|d }t|�rv| �|�S t |t�r�| �� | }n�t |t�r�|td �kr�| � � S t j t| �t j d�| }| � |�S t |t�r�t|�r�|�� }n t �|�}t| |�}t�|��r(| � t j t|�t j d�| �S t|d��r>| � |�S td|� d���t| �|| jd �S ) NrH r r� ztoo many indices for array.)r4 r� zCannot slice with 'r� )rl )rm �tuplerr �Ellipsis� IndexErrorr � _get_val_atrS �slicerv r5 Zarange�int32�taker/ r r6 r+ �comZis_bool_indexer�hasattrr9 rn rl )r� r� Z data_slicer� r: r: r; � __getitem__* s: zSparseArray.__getitem__c C sj t | �}|dk r||7 }||ks(|dk r0td��| j�|�}|dkrJ| jS | j| }t|| jj�}|S d S )Nr zOut of bounds accessr� )rr r� rP �lookupr7 rU r r4 )r� �loc�nZsp_loc�valr: r: r; r� Y s zSparseArray._get_val_at)� allow_fillr7 c C s� t |�rtd|� d���tj|tjd�}|jdkrLtjg dd�}d| ji}n,|rd| j||d�}i }n| � |�}d| ji}t | �|f| j | j d�|��S ) Nz*'indices' must be an array, not a scalar 'z'.)r4 r ru r4 )r7 )r7 rl ) r r9 r5 r6 r� �sizert r4 �_take_with_fill�_take_without_fillrn r7 rl )r� r� r� r7 rZ �kwargsr: r: r; r� i s zSparseArray.takec C s� |d kr| j j}|�� dk r$td��|�� t| �kr<td��t| �dkr�|dk�� r�t� | j t |��}tj ||d�}|� |� |S td��| j�|�}|dk}|dk| @ }| jjdkr�|�� r�tj|j| j| j jd�}n�| jjdk�rt� | j jt |��}tj|j||d�}n�| j �|�}|| dk } || dk } |j } | �� �rjt� | t | j��} |�| �}| j||< | �� �r�t� | t |��} |�| �}|||< |S )Nr� zKInvalid value in 'indices'. Must be between -1 and the length of the array.z!out of bounds value in 'indices'.r )r4 z.cannot do a non-empty take from an empty axes.)r7 r4 )r4 r� r� r9 �maxrr r� rF r5 r� rU rn Z empty_liker[ rP � lookup_arrayr} r� r� r7 r8 r� r� rO ) r� r� r7 r4 �taken� sp_indexerZnew_fill_indicesZold_fill_indicesr� Zm0�m1r� r: r: r; r� | sH zSparseArray._take_with_fillznp.ndarray | SparseArrayc C s |dk }|� � }t| �}|�� |ks2|�� | k rL|dkrDtd��ntd��|�� rd|| |7 < | jjdkr�tj |j | j t� t | j ��d�}t|| j d�\}}}t | �|||d�S | j�|�}| j�|�} |dk } | �� �rt� | t | j ��} | �| �} | j | | < | S )Nr z.cannot do a non-empty take from an empty axes.z!out of bounds value in 'indices'.)r4 )r7 )re r7 )rv rr r� r� r� r� rP r} r5 r� r� r7 r� rn r| r� rU r� rO ) r� r� Zto_shiftr� r� r2 rP r7 r� r� Zfillabler� r: r: r; r� � s0 z SparseArray._take_without_fillr> c C sL d}t j|tdd� t|�s&t�|�}t�|�}tj| | jjd��|||�S )Nz(searchsorted requires high memory usage.rC )rj )r4 ) rx ry r r r5 r6 r4 r8 r� )r� �vZsideZsorterr� r: r: r; r� � s zSparseArray.searchsorted)r� r3 c C s | j �� }| �|| j| j�S )N)rU rv r� rP r4 )r� r� r: r: r; rv � s zSparseArray.copyzSequence[SparseArrayT])r� � to_concatr3 c C s* |d j }g }d}|r"|d j}nd}|dkr�g }xF|D ]>}|j�� j�� }||7 }||jj7 }|�|j� |�|� q8W t � |�} t � |�}t ||�} n�g } g } xN|D ]F}|j� � }|�|j� | �|j �� | � | �|j� ||jj7 }q�W t � |�} t � | �} t � | �} t|| | �} | | | |d�S )Nr ri )re r7 )r7 rl rP r� r� rv rs �appendrU r5 Z concatenater Zto_block_index�blocs�blengthsr ) r� r� r7 r� rs Zsp_kindr� r2 r� rf rP r� r� r: r: r; r� � s< z SparseArray._concat_same_typec C sh t || j�r |s| S | �� S | j�|�}t|j�}t| j|dd�}|| jkrX|rX|�� }| � || j |�S )a0 Change the dtype of a SparseArray. The output will always be a SparseArray. To convert to a dense ndarray with a certain dtype, use :meth:`numpy.asarray`. Parameters ---------- dtype : np.dtype or ExtensionDtype For SparseDtype, this changes the dtype of ``self.sp_values`` and the ``self.fill_value``. For other dtypes, this only changes the dtype of ``self.sp_values``. copy : bool, default True Whether to ensure a copy is made, even if not necessary. Returns ------- SparseArray Examples -------- >>> arr = pd.arrays.SparseArray([0, 0, 1, 2]) >>> arr [0, 0, 1, 2] Fill: 0 IntIndex Indices: array([2, 3], dtype=int32) >>> arr.astype(np.dtype('int32')) [0, 0, 1, 2] Fill: 0 IntIndex Indices: array([2, 3], dtype=int32) Using a NumPy dtype with a different kind (e.g. float) will coerce just ``self.sp_values``. >>> arr.astype(np.dtype('float64')) ... # doctest: +NORMALIZE_WHITESPACE [0.0, 0.0, 1.0, 2.0] Fill: 0.0 IntIndex Indices: array([2, 3], dtype=int32) Use a SparseDtype if you wish to be change the fill value as well. >>> arr.astype(SparseDtype("float64", fill_value=np.nan)) ... # doctest: +NORMALIZE_WHITESPACE [nan, nan, 1.0, 2.0] Fill: nan IntIndex Indices: array([2, 3], dtype=int32) T)rv ) r r� rv r4 Z update_dtyper Z_subtype_with_strr rU r� rP )r� r4 rv r8 rU r: r: r; rO % s 9 zSparseArray.astypec sv t � t�r� �� � t � tj�rD� �| j| j�}� fdd�| jD �}n � | j�}� fdd�| jD �}t| �|| j |d�S )a� Map categories using input correspondence (dict, Series, or function). Parameters ---------- mapper : dict, Series, callable The correspondence from old values to new. Returns ------- SparseArray The output array will have the same density as the input. The output fill value will be the result of applying the mapping to ``self.fill_value`` Examples -------- >>> arr = pd.arrays.SparseArray([0, 1, 2]) >>> arr.map(lambda x: x + 10) [10, 11, 12] Fill: 10 IntIndex Indices: array([1, 2], dtype=int32) >>> arr.map({0: 10, 1: 11, 2: 12}) [10, 11, 12] Fill: 10 IntIndex Indices: array([1, 2], dtype=int32) >>> arr.map(pd.Series([10, 11, 12], index=[0, 1, 2])) [10, 11, 12] Fill: 10 IntIndex Indices: array([1, 2], dtype=int32) c s g | ]}� � |d ��qS )N)�get)�.0�x)�mapperr: r; � <listcomp>� s z#SparseArray.map.<locals>.<listcomp>c s g | ] }� |��qS r: r: )r� r� )r� r: r; r� � s )re r7 ) rm r! �to_dictr �Mappingr� r7 rU rn rP )r� r� r7 rU r: )r� r; �map� s ( zSparseArray.mapc C s t j| | jjd�S )zr Convert SparseArray to a NumPy array. Returns ------- arr : NumPy array )r4 )r5 r6 rU r4 )r� r: r: r; rS � s zSparseArray.to_densec C sV t |t�rF|\}\}}t�g �}|�|� || _|| _t|j|�| _ n | j � |� dS )z*Necessary for making this object picklableN) rm r� r5 rt � __setstate__r rh r' r4 r� �__dict__�update)r� �stateZnd_stater7 rP r� r: r: r; r� � s zSparseArray.__setstate__c C s4 | j dkr| j�� jfS | j�� j| jdk fS d S )Nr )r7 rP r� r� rU )r� r: r: r; �nonzero� s zSparseArray.nonzero)�skipna)rA r� c K sp t | |d �}|d kr*td|� d| j� ���|r4| }n| �� }|�dd � |�dd � |�dd � t ||�f |�S )Nzcannot perform z with type Z filter_typeZ numeric_onlyr@ )rW ro r4 r� �pop)r� rA r� r� r� r2 r: r: r; �_reduce� s zSparseArray._reducec O s: t �||� | j}t|�t| �kr2t�| j�s2dS |�� S )z� Tests whether all elements evaluate True Returns ------- all : bool See Also -------- numpy.all F)�nvZ validate_allrU rr r5 rF r7 )r� �axis�argsr� r� r: r: r; rF � s zSparseArray.allr c O s> t �||� | j}t|�t| �kr2t�| j�r2dS |�� �� S )z� Tests whether at least one of elements evaluate True Returns ------- any : bool See Also -------- numpy.any T)r� Z validate_anyrU rr r5 r� r7 r� )r� r� r� r� r� r: r: r; r� s zSparseArray.anyr )r� � min_countr3 c O s~ t �||� | j}|�� }| jrBt|jd|�r>t| jj dd�S |S | j j }t|jd|| �rlt| jj dd�S || j | S dS )a Sum of non-NA/null values Parameters ---------- axis : int, default 0 Not Used. NumPy compatibility. min_count : int, default 0 The required number of valid values to perform the summation. If fewer than ``min_count`` valid values are present, the result will be the missing value indicator for subarray type. *args, **kwargs Not Used. NumPy compatibility. Returns ------- scalar NF)�compat) r� Z validate_sumr� �sumr� r- r� r# r4 r8 rP rQ r7 )r� r� r� r� r� � valid_vals�sp_sum�nsparser: r: r; r� s zSparseArray.sumc O s\ t �||� |dk r.|| jkr.td|� d���| jsDt| �� ��� S t| j�� | j | j d�S )a Cumulative sum of non-NA/null values. When performing the cumulative summation, any non-NA/null values will be skipped. The resulting SparseArray will preserve the locations of NaN values, but the fill value will be `np.nan` regardless. Parameters ---------- axis : int or None Axis over which to perform the cumulative summation. If None, perform cumulative summation over flattened array. Returns ------- cumsum : SparseArray Nzaxis(=z) out of bounds)re r7 ) r� Zvalidate_cumsum�ndimr9 r� r/ rS �cumsumrU rP r7 )r� r� r� r� r: r: r; r� < s zSparseArray.cumsumc O sR t �||� | j}|�� }t|�}| jr0|| S | jj}|| j| || S dS )zb Mean of non-NA/null values Returns ------- mean : float N) r� Z validate_meanr� r� rr r� rP rQ r7 )r� r� r� r� r� r� �ctr� r: r: r; �mean\ s zSparseArray.meanc O s8 t �||� | jdkr(| jjdkr(| jS t�| |�S d S )Nr )r� Z validate_maxr� r� r7 r5 Znanmax)r� r� r� r� r: r: r; r� o s zSparseArray.maxc O s8 t �||� | jdkr(| jjdkr(| jS t�| |�S d S )Nr )r� Z validate_minr� r� r7 r5 Znanmin)r� r� r� r� r: r: r; r� x s zSparseArray.minznp.ufunc)�ufuncr� c sT |� dd�}x&|| D ]}t|� jtf �stS qW tj� ||f|�|�}|tk rV|S t|�dkr�t||�� j f|�}t||�� j f|�} t|t �r�t � fdd�t || �D ��} | S t |�r�|S � �|� jt|j| ��S t||�dd� |D �|�}|�rt|�dk�r|d }|S t|�t k�r6t � fd d�|D ��S |d k�rDd S t� �|�S d S ) Nr� r: rH c 3 s* | ]"\}}� � |� jt|j|��V qd S )N)r� rP r' r4 )r� Zsp_valueZfv)r� r: r; � <genexpr>� s z.SparseArray.__array_ufunc__.<locals>.<genexpr>c S s g | ]}t �|��qS r: )r5 r6 )r� r� r: r: r; r� � s z/SparseArray.__array_ufunc__.<locals>.<listcomp>r c 3 s | ]}t � �|�V qd S )N)rn )r� r� )r� r: r; r � s �at)r� rm �_HANDLED_TYPESr/ �NotImplemented�opsZ!maybe_dispatch_ufunc_to_dunder_oprr rW rU r7 r� �zipr r� rP r' r4 rn ) r� r r� �inputsr� r� r� rZ rU r7 Zarraysr: )r� r; �__array_ufunc__� s< zSparseArray.__array_ufunc__c C s t �| �S )N)r5 r� )r� r: r: r; �__abs__� s zSparseArray.__abs__c C s* |j }t|t�r t| |||�S t|�r�tjdd��& |t| �t�|��}|| j |�}W d Q R X |dkr�|\}}|\}} t ||| j |�t ||| j | �fS t ||| j |�S t�|�}tjdd��d t | �t |�kr�t dt | �� dt |�� ���t|t��st|dd �} t|| j| d�}t| |||�S Q R X d S )NrE )rF �divmodzlength mismatch: z vs. r4 )r7 r4 )�__name__rm r/ r^ r r5 rR r< r6 rU rY rP rr r~ rW r7 ) r� �otherr@ �op_namer[ rZ r> r? ZlfillZrfillr4 r: r: r; � _arith_method� s, zSparseArray._arith_methodc C s� t |�s t|t| ��s t�|�}t|tj�rft| �t|�krXtdt| �� dt|�� ���t|| j d�}t|t�r�|j � d�}t | |||�S tj dd�� || j |�}|| j|�}W d Q R X t| �|| j|tjd�S d S )Nzlength mismatch: z vs. )r7 rM rE )rF )re r7 r4 )r rm rn r5 r6 rw rr r~ r/ r7 r �stripr^ rR rU rP Zbool_)r� r r@ r r7 rZ r: r: r; � _cmp_method� s" zSparseArray._cmp_methodc C s> |t �| j���� }|| j�}t|j|�}t| ��|| j |�S )N) r5 rt r7 r� rU r' r4 rn r� rP )r� r@ r7 r� r4 r: r: r; � _unary_method� s zSparseArray._unary_methodc C s | � tj�S )N)r �operator�pos)r� r: r: r; �__pos__ s zSparseArray.__pos__c C s | � tj�S )N)r r �neg)r� r: r: r; �__neg__ s zSparseArray.__neg__c C s | � tj�S )N)r r �invert)r� r: r: r; � __invert__ s zSparseArray.__invert__c C s6 t �| �}t �| j�}t �| j�}|� d|� d|� �S )Nz Fill: � )�printingZ pprint_thingr7 rP )r� Zpp_strZpp_fillZpp_indexr: r: r; �__repr__ s zSparseArray.__repr__c C s d S )Nr: )r� Zboxedr: r: r; � _formatter s zSparseArray._formatter)NNNri NF)N)NNN)rH N)r� )T)N)r> N)NT)N)r )r r )r )r )r )r )F)Mr � __module__� __qualname__�__doc__Z_subtypr( Z _hidden_attrs� frozenset�__annotations__r� � classmethodr� r� r� r� r� r� �propertyrP rU r4 r7 �setterrl r� r� r� r� r� r� r} r" r� r� r� r� r� r� r� r� r� r� r� r� r� rv r� rO r� rS Z_internal_get_valuesr� r� r� rF r� r� r� r r� r� r5 rw �numbers�Numberr r r r r Z_logical_methodr r r r r r r: r: r: r; r/ � s� E . 7 $/A& 6^4 2"r� zNpDtype | None)r2 r4 c C s� t | tj�st�| jdkr"td��|dkr4t| j�}t|�rFt | �}n4t | j�rZ| � t �} t | j�rrt�| |�}n| |k}t| �}|t|�kr�|jj}n|�� d � tj�}t|||�}| | }|dk r�t||d�}|||fS )a@ Convert ndarray to sparse format Parameters ---------- arr : ndarray kind : {'block', 'integer'} fill_value : NaN or another value dtype : np.dtype, optional copy : bool, default False Returns ------- (sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar) rH z expected dimension <= 1 dataNr )r4 )rm r5 rw r~ r� ro r# r4 r" r$ r rO ru r rX Zmake_mask_object_ndarrayrr rP r� r� r� �make_sparse_indexr ) r2 rl r7 r4 r� rs r� r\ Zsparsified_valuesr: r: r; r| s, r| c C sX |dkst |t�r.t�|�\}}t| ||�}n&|dks@t |t�rLt| |�}ntd��|S )Nr� ri z must be block or integer type)rm r rX Z get_blocksr r9 )rs r� rl ZlocsZlensr\ r: r: r; r( X s r( )N)r� NN)`r � __future__r � collectionsr r& r �typingr r r r rx �numpyr5 Z pandas._libsr Zpandas._libs.sparseZ_libs�sparserX r r r Zpandas._libs.tslibsr Zpandas._typingr r r Zpandas.compat.numpyr r� Z pandas.errorsr Zpandas.core.dtypes.castr r r r Zpandas.core.dtypes.commonr r r r r r r r r r Zpandas.core.dtypes.genericr r! Zpandas.core.dtypes.missingr" r# r$ Zpandas.core.algorithms�coreZ algorithmsr� Zpandas.core.arrayliker% Zpandas.core.arraysr& Z pandas.core.arrays.sparse.dtyper' Zpandas.core.baser( Zpandas.core.common�commonr� Zpandas.core.constructionr) r* Zpandas.core.indexersr+ Zpandas.core.missingr, Zpandas.core.nanopsr- Zpandas.core.opsr Zpandas.io.formats.printing�io�formatsr r. Z_sparray_doc_kwargsr< r^ rY r/ r| r( r: r: r: r; �<module> s` 0 Z H;
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