Файловый менеджер - Редактировать - /home/digitalm/venv/lib/python3.7/site-packages/pandas/core/internals/__pycache__/construction.cpython-37.pyc
Назад
B �5�g�x � @ sP d Z ddlmZ ddlmZ ddlmZmZmZm Z m Z ddl Z ddl Z ddlmZ ddlmZ ddlmZmZmZ ddlmZmZmZmZmZm Z dd l m Z m Z m Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z& dd l'm(Z( dd l)m*Z*m+Z+ dd l,m-Z-m.Z/ dd l0m1Z1m2Z2m3Z3m4Z4 ddl5m6Z6m7Z7m8Z8m9Z9 ddl:m;Z< ddl=m>Z>m?Z?m@Z@mAZAmBZBmCZC ddlDmEZEmFZF ddlGmHZHmIZI ddlJmKZKmLZLmMZMmNZN e�r�ddlOmPZP ddddd�ddddddd �d d �ZQd ddd d!�d"d#�ZRd$dd%d&�d'd(�ZSdqd dd)�d*d+�ZTddd dd,�d-d.�ZUd/ddd0d1�d2d3�ZVdd4dd5�d6dd ddd7�d8d9�ZWd:d;d;dd<d=�d>d?�ZXdd@�dAdB�ZYdrdd/dC�dDdE�ZZdddFdG�dHdI�Z[dd@�dJdK�Z\dFdd;dLdM�dNdO�Z]dd@�dPdQ�Z^dRdRd;d;dSdT�dUdV�Z_dWdX� Z`dsd;ddLdY�dZd[�Zad\d/d]�d^d_�Zbd`d;dadb�dcdd�Zcded;dadb�dfdg�Zdd/d;ddLdh�didj�Zed%d;ddk�dldm�Zfd%ddFdn�dodp�ZgdS )tz~ Functions for preparing various inputs passed to the DataFrame or Series constructors before passing them to a BlockManager. � )� annotations)�abc)� TYPE_CHECKING�Any�Hashable�Sequence�castN)�lib)� ArrayLike�DtypeObj�Manager)�"construct_1d_arraylike_from_scalar� dict_compat�maybe_cast_to_datetime�maybe_convert_platform�maybe_infer_to_datetimelike� maybe_upcast) �is_1d_only_ea_dtype�is_datetime64tz_dtype� is_datetime_or_timedelta_dtype�is_dtype_equal�is_extension_array_dtype�is_integer_dtype� is_list_like�is_named_tuple�is_object_dtype)�ExtensionDtype)� ABCDataFrame� ABCSeries)� algorithms�common)� Categorical� DatetimeArray�ExtensionArray�TimedeltaArray)� ensure_wrapped_if_datetimelike� extract_array�range_to_ndarray�sanitize_array)�base)� DatetimeIndex�Index�TimedeltaIndex� ensure_index�get_objs_combined_axis� union_indexes)� ArrayManager�SingleArrayManager)�ensure_block_shape� new_block)� BlockManager�SingleBlockManager� create_block_manager_from_arrays� create_block_manager_from_blocks)� MaskedRecordsT)�dtype�verify_integrity�typ� consolidater+ zDtypeObj | None�boolz str | Noner )� arr_namesr9 r: r; r< �returnc C s� |r,|dkrt | �}nt|�}t| ||�} nt|�}t|�}||g}|dkr\t| |||d�S |dkr�t|�t| �kr�t| �dks�t�dd� tt|��D �} t| ||g�S td|� d ���dS ) zs Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases. N�block)r< �arrayr c S s g | ]}t jg td ��qS ))r9 )�nprA �object)�.0�_� rF �X/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/internals/construction.py� <listcomp>� s z!arrays_to_mgr.<locals>.<listcomp>z2'typ' needs to be one of {'block', 'array'}, got '�') �_extract_indexr- � _homogenizer6 �len�AssertionError�ranger0 � ValueError) �arraysr> �index�columnsr9 r: r; r< �axesrF rF rG � arrays_to_mgrd s" rT z(MaskedRecords | np.recarray | np.ndarray�str)�datar9 �copyr; c C s� t �| �}|dkr t|�}nt|�}|dk r4t|�}t||�\}}t| tj j�rftd| �} t | |�} n|} t | ||�\}}|dkr�|}t ||||||d�} |r�| � � } | S )zA Extract from a masked rec array and create the manager. Nr8 )r9 r; ) �maZgetdata�_get_names_from_indexr- � to_arrays� isinstancerB Z MaskedArrayr �fill_masked_arrays�reorder_arraysrT rW ) rV rQ rR r9 rW r; �fdatarP � arr_columns� new_arrays�mgrrF rF rG �rec_array_to_mgr� s& rb r8 zlist[np.ndarray])rV r_ r? c C sZ g }xP|D ]H}| | }|j }t�|�}|�� rHt||dd�\}}|||< |�|� q W |S )zA Convert numpy MaskedRecords to ensure mask is softened. T)� fill_valuerW )rc rX Z getmaskarray�anyr �append)rV r_ r` �col�arrZfv�maskrF rF rG r\ � s r\ )r; rW c s |dkr`t � t�r� }nF� jdkrJt� j� jd � jd � jd dd�}nt�� jd � j�}n�|dkr�t � t �rx� }n|� jdkrЇ fdd�t t � jd ��D �}|r�d d� |D �}t |� jd � jd g�}n$� � � }|r�|� � }t|g� jg�}ntd |� d ���|S ) z� Convert to specific type of Manager. Does not copy if the type is already correct. Does not guarantee a copy otherwise. `copy` keyword only controls whether conversion from Block->ArrayManager copies the 1D arrays. r@ � r � )r; rA c s g | ]}� � |��qS rF )Z iget_values)rD �i)ra rF rG rH � s z mgr_to_mgr.<locals>.<listcomp>c S s g | ] }|� � �qS rF )rW )rD rg rF rF rG rH � s z2'typ' needs to be one of {'block', 'array'}, got 'rI )r[ r4 �ndimrT rP rS r5 Z from_arrayrQ r0 rN rL Zinternal_valuesrW r1 rO )ra r; rW Znew_mgrrP rA rF )ra rG � mgr_to_mgr� s* & rm )r9 rW r; r? c s@ t �t�rh|d kr(�jd k r(t�jg�}|d kr8�j}n ��|��t��sh|d k rht|�rhtjdt d��t �dd �}t |�s�t |t �r�t �tj �r��jdkr��fdd�t�jd �D ��n�g�|d kr�ttt����}nt|�}t�|||||d�S t|��r>t |��s>t�dd ��|�r$��� ��jdk�rJ��d d��n t�|d ��|d k �r�t�j|��s��j}��� }t|��o��jj d k } t |d ||| d ����|��t �jd �jd ||d�\}}t �||� |dk�rnt �jj!t"��r�tj#�t d��|d k�r,t$�j��r,�fdd�t�jd �D �} n0t%�j��r@t&����fdd�t�jd �D �} t'| ||gdd�S �j(�|d k�rt$�j��r�jdk�rވjd dk�r�dd� �D �} dd� | D �� � fdd�tt� ��D �} n$t)��} t*| t+t|��dd�}|g} n t*�t+t|��dd�}|g} t|�dk�r2g } t,| ||g�S )N)r rj )r9 r9 rj c s g | ]}� d d �|f �qS )NrF )rD �n)�valuesrF rG rH s z"ndarray_to_mgr.<locals>.<listcomp>)r9 r; T)� extract_numpy���)rW �f)r9 rW �raise_cast_failurer )rQ rR rA c s( g | ] }t t� d d �|f �� ���qS )N)r% r rW )rD rk )ro rF rG rH M s c s g | ]}� d d �|f � � �qS )N)rW )rD rk )ro rF rG rH U s F)r: ri c S s g | ] }t |��qS rF )r )rD �rowrF rF rG rH c s c S s g | ]}t |d ��qS )ri )r2 )rD �dvalrF rF rG rH d s c s g | ]}t � | |d d��qS )ri )� placementrl )r3 )rD rn )� dvals_listrF rG rH h s )rv rl )-r[ r �namer+ rQ �reindexrL rB �emptyrC �getattrr r �ndarrayrl rN �shaper- rT r r& rW �reshape� _prep_ndarrayr r9 Zravelr �kindr( � _get_axes�!_check_values_indices_shape_match� issubclass�typerU rA r r r% r0 �Tr r3 �slicer7 )ro rQ rR r9 rW r; Zvdtyper} ZflatZrcfrP Z dtlike_valsZ block_valuesZ datelike_vals�nbrF )rw ro rG �ndarray_to_mgr sx r� z np.ndarray�None)ro rQ rR r? c C sh | j d t|�ks$| j d t|�krd| j d dkr:td��| j }t|�t|�f}td|� d|� ���dS )z\ Check that the shape implied by our axes matches the actual shape of the data. rj r z)Empty data passed with indices specified.zShape of passed values is z, indices imply N)r} rL rO )ro rQ rR ZpassedZimpliedrF rF rG r� z s $r� r@ )r9 r; rW �dict)rV r9 r; rW r? c s2 |dk r�ddl m} |� |td�}|j}|�� } |dkrHt|| �}nt|�}| �� r�t|�s�|dks�t |t j �r�t � |t j �r�t � d�} n|} tt jt|�| �} | g| �� |j| < t|�}nFt� �� �} t| � }}� fdd�| D �}dd� |D �}d d� |D �}|�r d d� |D �}t|||||||d �S ) z� Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases. Used in DataFrame.__init__ Nr )�Series)rQ r9 rC c s g | ]}t �� | ��qS rF )�comZmaybe_iterable_to_list)rD �k)rV rF rG rH � s z dict_to_mgr.<locals>.<listcomp>c S s g | ]}t |t�s|n|j�qS rF )r[ r+ �_data)rD rg rF rF rG rH � s c S s g | ]}t |�s|n|�� �qS rF )r rW )rD rg rF rF rG rH � s c S s. g | ]&}t |d �r t|jt�s"|n|�� �qS )r9 )�hasattrr[ r9 r rW )rD �xrF rF rG rH � s )r9 r; r< )Zpandas.core.seriesr� rC rQ ZisnarJ r- rd r r[ rB r9 Z issubdtype�flexibler �nanrL �sum�loc�list�keysr+ rT ) rV rQ rR r9 r; rW r� rP Z data_names�missingZ nan_dtype�valr� rF )rV rG � dict_to_mgr� s4 r� r z Index | Nonez$tuple[list[ArrayLike], Index, Index])rV rR rQ r9 r? c C s� t | d �r"|dkr"t| d j�}t| ||d�\}}t|�}|dkr�t| d t�r\t| �}n0t| d t�r~t� t | d ��}nt� t | ��}|||fS )zA Convert a single sequence of arrays to multiple arrays. r N)r9 ) r r- �_fieldsrZ r[ r rY r! �ibase� default_indexrL )rV rR rQ r9 rP rF rF rG �nested_data_to_arrays� s r� )r? c C sB t | �dko@t| d �o@t| d dd�dko@t| t�o>| jdk S )z7 Check if we should use nested_data_to_arrays. r rl rj ri )rL r r{ r[ r# rl )rV rF rF rG �treat_as_nested� s r� )rW r? c s0 t | t�s t | t�r$| jd kr$| j} t | tjttf�s�t | �dkrPtj dt d�S t | t �rpt | �}|dtjf S dd� � t| d �r�t�� fdd�| D ��} q�t | d tj�r�| d jdkr�t�� fd d�| D ��} q� | �} ntj| |d �} | jd k�r| �| jd d f�} n | jd k�r,td | j� ���| S )Nr )r r )r9 .c S s. t | �rt| t�r| S t| dd�} t| �}|S )NT)rp )r r[ r r& r )�v�resrF rF rG �convert s z _prep_ndarray.<locals>.convertc s g | ] }� |��qS rF rF )rD r� )r� rF rG rH s z!_prep_ndarray.<locals>.<listcomp>c s g | ] }� |��qS rF rF )rD r� )r� rF rG rH s )rW rj ri zMust pass 2-d input. shape=)r[ r$ r" �tzZ_ndarrayrB r| r r+ rL rz rC rN r' Znewaxisr rA rl r~ r} rO )ro rW rg rF )r� rG r s* r zlist[ArrayLike])rQ r9 r? c C s� d }g }x�| D ]�}t |t�rR|d k r2|j|dd�}|j|k rJ|j|dd�}|j}nbt |t�r�|d krn|�d�}t |ttf�r�t |�}nt|�}t j ||jt j d�}t|||ddd�}|�|� qW |S )NF)rW �O)�default)r9 rW rs )r[ r ZastyperQ ry Z_valuesr� r* r, r r Z fast_multigetrB r� r( re )rV rQ r9 ZoindexZ homogenizedr� rF rF rG rK 0 s( rK c C sv d}t | �dkr tg �}�nRt | �dk�rng }g }d}d}d}xt| D ]l}t|t�rdd}|�|j� qDt|t�r�d}|�t|�� �� qDt |�rDt |dd�dkrDd}|�t |�� qDW |s�|s�t d��|r�t |�}n|r�t |dd�}|�rntt |��}t |�dk�r t d ��|�rt d ��|�r`|dk �s,t�|d t |�k�rnd |d � d t |�� �} t | ��nt�|d �}t|�S ) zR Try to infer an Index from the passed data, raise ValueError on failure. Nr FTrl rj z2If using all scalar values, you must pass an index)�sortz%All arrays must be of the same lengthz<Mixing dicts with non-Series may lead to ambiguous ordering.z array length z does not match index length )rL r+ r[ r re rQ r� r� r� r r{ rO r/ �setrM r� r� r- ) rV rQ Z raw_lengthsZindexesZhave_raw_arraysZ have_seriesZ have_dictsr� �lengths�msgrF rF rG rJ V sN rJ z tuple[list[ArrayLike], Index])rP r_ rR r? c sV |d k rNt |�rNt � �rNt� ��|�}t� fdd�|D ��� �fdd�|D ���� fS )Nc s g | ] }� | �qS rF rF )rD rk )r_ rF rG rH � s z"reorder_arrays.<locals>.<listcomp>c s g | ] }� | �qS rF rF )rD rk )rP rF rG rH � s )rL r- � get_indexer)rP r_ rR �indexerrF )r_ rP rG r] � s r] c C s� t dd� | D ��}|s$t�t| ��S ttt| ���}d}xHt| �D ]<\}}t|dd �}|d k rh|||< qBd|� �||< |d7 }qBW t|�S )Nc s s | ]}t |d d�dk V qdS )rx N)r{ )rD �srF rF rG � <genexpr>� s z(_get_names_from_index.<locals>.<genexpr>r rx zUnnamed rj ) rd r� r� rL r� rN � enumerater{ r+ )rV Z has_some_namerQ �countrk r� rn rF rF rG rY � s rY �intztuple[Index, Index])�N�KrQ rR r? c C s@ |d krt �| �}nt|�}|d kr0t �|�}nt|�}||fS )N)r� r� r- )r� r� rQ rR rF rF rG r� � s r� c C s ddl m} tt|| ��S )a� Converts a list of dataclass instances to a list of dictionaries. Parameters ---------- data : List[Type[dataclass]] Returns -------- list_dict : List[dict] Examples -------- >>> @dataclass >>> class Point: ... x: int ... y: int >>> dataclasses_to_dicts([Point(1,2), Point(2,3)]) [{"x":1,"y":2},{"x":2,"y":3}] r )�asdict)� dataclassesr� r� �map)rV r� rF rF rG �dataclasses_to_dicts� s r� )rR r9 r? c s� t �t�rV� dk r.� �fdd�t�j�D �}n �j� �fdd�tt� ��D �}|� fS t��s�t �tj�r܈jj dk r�t �jj �� �fdd�� D �}t��dkr�x2t|�D ]&\}}|j dkr�|dd�df ||< q�W |� fS g t g �fS t �d t ��r(t jdtd d � � dk�r t�t���� �� fS t �tj��rn�jj dk �rntt�jj ��� �fd d�� D �}|� fS t �d ttf��r�t��}nXt �d tj��r�t�� �\}� n6t �d t��r�t�� �\}� nd d� �D ��t��}t|� |�\}� |� fS ) z) Return list of arrays, columns. Nc s( g | ] \}}|� kr�j |d d�j�qS )rj )�axis)�_ixsro )rD rk rf )rR rV rF rG rH � s z to_arrays.<locals>.<listcomp>c s g | ]}� j |d d�j�qS )rj )r� )r� ro )rD rk )rV rF rG rH � s c s g | ] }� | �qS rF rF )rD rx )rV rF rG rH � s r ri z�The behavior of DataFrame([categorical, ...]) is deprecated and in a future version will be changed to match the behavior of DataFrame([any_listlike, ...]). To retain the old behavior, pass as a dictionary DataFrame({col: categorical, ..})� )� stacklevelc s g | ] }� | �qS rF rF )rD r� )rV rF rG rH s c S s g | ] }t |��qS rF )�tuple)rD r� rF rF rG rH $ s ) r[ r r� rR rN rL rB r| r9 �namesr- rl r! �warnings�warn� FutureWarningr� r� r+ r� r� �_list_to_arraysr �Mapping�_list_of_dict_to_arraysr �_list_of_series_to_arrays�_finalize_columns_and_data)rV rR r9 rP rk rg �contentrF )rR rV rG rZ � sP rZ zlist[tuple | list])rV r? c C s( t | d t�rt�| �}n t�| �}|S )Nr )r[ r� r Zto_object_array_tuplesZto_object_array)rV r� rF rF rG r� + s r� r� ztuple[np.ndarray, Index])rV rR r? c C s� |d kr"dd� | D �}t |dd�}i }g }x|| D ]t}t|dd �}|d krVt�t|��}t|�|krp|t|� }n|�|� }|t|�< t|dd�}|�t � ||�� q0W t � |�} | |fS )Nc S s g | ]}t |ttf�r|�qS rF )r[ r r )rD r� rF rF rG rH > s z-_list_of_series_to_arrays.<locals>.<listcomp>F)r� rQ T)rp ) r. r{ r� r� rL �idr� r& re r Ztake_ndrB Zvstack) rV rR Z pass_dataZ indexer_cacheZaligned_valuesr� rQ r� ro r� rF rF rG r� 6 s r� z list[dict]c C sf |dkr@dd� | D �}t dd� | D �� }tj||d�}t|�}dd� | D �} t�| t|��}||fS )a Convert list of dicts to numpy arrays if `columns` is not passed, column names are inferred from the records - for OrderedDict and dicts, the column names match the key insertion-order from the first record to the last. - For other kinds of dict-likes, the keys are lexically sorted. Parameters ---------- data : iterable collection of records (OrderedDict, dict) columns: iterables or None Returns ------- content : np.ndarray[object, ndim=2] columns : Index Nc s s | ]}t |�� �V qd S )N)r� r� )rD r� rF rF rG r� r s z*_list_of_dict_to_arrays.<locals>.<genexpr>c s s | ]}t |t�V qd S )N)r[ r� )rD �drF rF rG r� s s )r� c S s$ g | ] }t |�tkr|nt|��qS rF )r� r� )rD r� rF rF rG rH y s z+_list_of_dict_to_arrays.<locals>.<listcomp>)rd r Z fast_unique_multiple_list_genr- Zdicts_to_arrayr� )rV rR �genr� Zpre_colsr� rF rF rG r� Z s r� )r� rR r9 r? c C sr t | j�}yt||�}W n, tk rD } zt|�|�W dd}~X Y nX t|�rj|d jtjkrjt ||d�}||fS )zG Ensure we have valid columns, cast object dtypes if possible. Nr )r9 ) r� r� � _validate_or_indexify_columnsrM rO rL r9 rB Zobject_�_convert_object_array)r� rR r9 �contents�errrF rF rG r� s r� )r� rR r? c C s� |dkrt �t| ��}n�t|t�o2tdd� |D ��}|sft|�t| �krftt|�� dt| �� d���n\|r�tdd� |D ��dkr�td ��n8|r�t|d �t| �kr�tt|d �� dt| �� d���|S ) a� If columns is None, make numbers as column names; Otherwise, validate that columns have valid length. Parameters ---------- content : list of np.ndarrays columns : Index or None Returns ------- Index If columns is None, assign positional column index value as columns. Raises ------ 1. AssertionError when content is not composed of list of lists, and if length of columns is not equal to length of content. 2. ValueError when content is list of lists, but length of each sub-list is not equal 3. ValueError when content is list of lists, but length of sub-list is not equal to length of content Nc s s | ]}t |t�V qd S )N)r[ r� )rD rf rF rF rG r� � s z0_validate_or_indexify_columns.<locals>.<genexpr>z! columns passed, passed data had z columnsc S s h | ] }t |��qS rF )rL )rD rf rF rF rG � <setcomp>� s z0_validate_or_indexify_columns.<locals>.<setcomp>rj z<Length of columns passed for MultiIndex columns is differentr )r� r� rL r[ r� �allrM rO )r� rR Z is_mi_listrF rF rG r� � s r� )r� r9 r? c s" �fdd�� � fdd�| D �}|S )z� Internal function to convert object array. Parameters ---------- content: List[np.ndarray] dtype: np.dtype or ExtensionDtype Returns ------- List[ArrayLike] c s& � t �d�kr"t�| �} t| � �} | S )Nr� )rB r9 r Zmaybe_convert_objectsr )rg )r9 rF rG r� � s z&_convert_object_array.<locals>.convertc s g | ] }� |��qS rF rF )rD rg )r� rF rG rH � s z)_convert_object_array.<locals>.<listcomp>rF )r� r9 rP rF )r� r9 rG r� � s r� )T)T)N)h�__doc__� __future__r � collectionsr �typingr r r r r r� �numpyrB Znumpy.marX Z pandas._libsr Zpandas._typingr r r Zpandas.core.dtypes.castr r r r r r Zpandas.core.dtypes.commonr r r r r r r r r Zpandas.core.dtypes.dtypesr Zpandas.core.dtypes.genericr r Z pandas.corer r r� Zpandas.core.arraysr! r" r# r$ Zpandas.core.constructionr% r& r' r( Zpandas.core.indexesr) r� Zpandas.core.indexes.apir* r+ r, r- r. r/ Z#pandas.core.internals.array_managerr0 r1 Z pandas.core.internals.blocksr2 r3 Z pandas.core.internals.managersr4 r5 r6 r7 Znumpy.ma.mrecordsr8 rT rb r\ rm r� r� r� r� r� r rK rJ r] rY r� r� rZ r� r� r� r� r� r� rF rF rF rG �<module> sd , &2)z B /&= !H $%:
| ver. 1.4 |
Github
|
.
| PHP 8.3.23 | Генерация страницы: 0.07 |
proxy
|
phpinfo
|
Настройка