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B �5�gx� � @ s� d dl mZ d dlZd dlmZmZ d dlZd dlm m Z d dl m Z d dlmZ d dlmZ d dlmZ d dlmZmZmZmZmZmZmZm Z m Z d d l m Z d d l m!Z! d dl"m# m$Z% d d l&m'Z' d d l(m)Z) d d l*m+Z+ d dl,m-Z- d dl.m/Z/m0Z0 d dl1m2Z2 d dl3m4Z4m5Z5m6Z6m7Z7m8Z8 e�r>d dl&m9Z9 G dd� d�Z:d8dd�Z;d9dd�Z<d:dd�Z=dd � Z>d;d d �Z?d<d!d"�Z@d#d#d$�d%d&�ZAd=d'd(�ZBd>d+d+d+d,d-d.�d/d0�ZCd?d+d+d+d,d-d.�d1d2�ZDd3d4d4d3d5�d6d7�ZEdS )@� )� annotationsN)� TYPE_CHECKING�cast)�IntIndex)�Dtype)�cache_readonly)� maybe_promote) �ensure_platform_int�is_1d_only_ea_dtype� is_bool_dtype�is_extension_array_dtype� is_integer�is_integer_dtype� is_list_like�is_object_dtype�needs_i8_conversion)�ExtensionDtype)�notna)� SparseArray)�factorize_from_iterable)� ensure_wrapped_if_datetimelike)� DataFrame)�Index� MultiIndex)�Series)�compress_group_index�decons_obs_group_ids�get_compressed_ids�get_group_index�get_group_index_sorter)�ExtensionArrayc @ s| e Zd ZdZd dd�dd�Zedd �d d ��Zed d � �Zddd�dd�Zdd� Z dd� Z d dd�Z dd� Z edd� �Z dS ) � _Unstackera Helper class to unstack data / pivot with multi-level index Parameters ---------- index : MultiIndex level : int or str, default last level Level to "unstack". Accepts a name for the level. fill_value : scalar, optional Default value to fill in missing values if subgroups do not have the same set of labels. By default, missing values will be replaced with the default fill value for that data type, NaN for float, NaT for datetimelike, etc. For integer types, by default data will converted to float and missing values will be set to NaN. constructor : object Pandas ``DataFrame`` or subclass used to create unstacked response. If None, DataFrame will be used. Examples -------- >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), ... ('two', 'a'), ('two', 'b')]) >>> s = pd.Series(np.arange(1, 5, dtype=np.int64), index=index) >>> s one a 1 b 2 two a 3 b 4 dtype: int64 >>> s.unstack(level=-1) a b one 1 2 two 3 4 >>> s.unstack(level=0) one two a 1 3 b 2 4 Returns ------- unstacked : DataFrame ���Nr )�indexc C s� |d kr t }|| _|�� | _| j�|�| _d| jj| j kr@dnd| _t| jj �| _ t| jj �| _ | j � | j�| _| j � | j�| _|j | j | _t�dd� | j D ��}| jj}tj||tjd�}|dkr�|dkr�|dkr�td��| �� d S )Nr" � r c S s g | ] }|j �qS � )�size)�.0Z index_levelr% r% �Q/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/reshape/reshape.py� <listcomp>| s z'_Unstacker.__init__.<locals>.<listcomp>)�dtypez6Unstacked DataFrame is too big, causing int32 overflow)r � constructorZremove_unused_levelsr# �_get_level_number�level�codes�lift�list�levels�new_index_levels�names�new_index_names�pop� removed_name� removed_level�removed_level_full�np�maxr& �multiplyZint32� ValueError�_make_selectors)�selfr# r- r+ Znum_rowsZ num_columnsZ num_cellsr% r% r( �__init__c s" z_Unstacker.__init__z#tuple[np.ndarray, list[np.ndarray]])�returnc C s� | j }t| jj�}t| jj�}|d |� ||d d � || g }tdd� |d |� ||d d � || g D ��}t||�\}}t|�}t||�} | |fS )Nr$ c s s | ]}t |�V qd S )N)�len)r' �xr% r% r( � <genexpr>� s z2_Unstacker._indexer_and_to_sort.<locals>.<genexpr>) r- r0 r# r. r1 �tupler rA r ) r> �vr. �levs�to_sort�sizes� comp_index�obs_ids�ngroups�indexerr% r% r( �_indexer_and_to_sort� s &4 z _Unstacker._indexer_and_to_sortc s | j \� }� fdd�|D �S )Nc s g | ]}|� � ��qS r% )�take)r' �line)rL r% r( r) � s z,_Unstacker.sorted_labels.<locals>.<listcomp>)rM )r> rG r% )rL r( � sorted_labels� s z_Unstacker.sorted_labelsz np.ndarray)�valuesr@ c C s | j \}}tj||dd�}|S )Nr )�axis)rM �algosZtake_nd)r> rQ rL �_� sorted_valuesr% r% r( �_make_sorted_values� s z _Unstacker._make_sorted_valuesc C s� | j }| jd d� }tdd� |D ��}t||�\}}t|�}t|�}| jj| j | j }||f| _ | jd || | j }t j t � | j �td�} | �|d� | �� t| j�k r�td��|| _| | _|| _|�t �|��| _d S )Nr" c s s | ]}t |�V qd S )N)rA )r' rB r% r% r( rC � s z-_Unstacker._make_selectors.<locals>.<genexpr>)r* Tz0Index contains duplicate entries, cannot reshape)r2 rP rD r rA r r# Zlevshaper- r/ � full_shaper9 �zeros�prod�bool�put�sumr< � group_index�maskZ unique_groupsZ searchsorted�arange� compressor) r> � new_levelsZremaining_labelsZ level_sizesrI rJ rK �stride�selectorr^ r% r% r( r= � s" z_Unstacker._make_selectorsc C sj |j dkr |d d �tjf }|d kr:|jd dkr:td��| �||�\}}| �|�}| j}| j|||d�S )Nr$ z-must pass column labels for multi-column data)r# �columns) �ndimr9 �newaxis�shaper< �get_new_values�get_new_columns� new_indexr+ )r> rQ � value_columns� fill_valuerT rd r# r% r% r( � get_result� s z_Unstacker.get_resultc C s� |j dkr |d d �tjf }| �|�}| j\}}|jd }|| }||f}| j} | �� } | r�t|�r�|� |||�� dd�� |�} tj |t d�} | | fS | r�|j } tj|| d�} t� | �j}njt|j |�\} }t| t��r| �� }|j|| d�} || d d �< | j}n$tj|| d�} | �|� t� | �j}tj|t d�} t|j ��rV|�d�}| �d�} n0t|j ��rx|�d�}| �d�} n|j|dd�}t� || �d�|||| | �d�� t|j ��r�| �d �} t | �} | �|j �} | | fS ) Nr$ � )r* �i8�objectF)�copy�u1zM8[ns]) re r9 rf rV rW rg r^ �allrA �reshapeZswapaxes�onesrZ r* �empty�namer � isinstancer �construct_array_type�_empty�fillrX r �viewr Zastype� libreshape�unstackr )r> rQ rl rU �length�widthrb Z result_widthZ result_shaper^ Zmask_all� new_valuesZnew_maskr* rw �clsr% r% r( rh � sb z_Unstacker.get_new_valuesc s. |d krB| j dkr"| jj| jd�S | jjd| jjd�}|�| j�S t| j�| j }t|�}t� t� |�|�� t |t �r�|j | jf }|j| jf }� fdd�|jD �}n || jg}|j| jg}� g}t| j�t| j�kr�| j�| j�}| j r�t�|dd�}nt� |�| j }|�t�||�� t |||dd�S ) Nr )rw )�itemc s g | ]}|� � ��qS r% )rN )r' �lab)� propagatorr% r( r) . s z._Unstacker.get_new_columns.<locals>.<listcomp>r" F)r1 r. r3 �verify_integrity)r/ r7 �_renamer6 �insert� _na_value�renamerA r9 �repeatr_ rx r r1 r8 r3 r. rw Z get_indexer�append�tile) r> rk �levrb r� ra � new_names� new_codesZrepeaterr% )r� r( ri s. z_Unstacker.get_new_columnsc s� � fdd�� j d d� D �}t� j�dkrr� jd |d }}|dk�� r\|�t|�|j�}|�|��� jd �S t � j|� jdd�S )Nc s g | ]}|� � j��qS r% )rN r` )r' r� )r> r% r( r) G s z(_Unstacker.new_index.<locals>.<listcomp>r" r$ r F)r1 r. r3 r� ) rP rA r2 �anyr� r� rN r� r4 r )r> Z result_codesr- � level_codesr% )r> r( rj D s z_Unstacker.new_index)r" N)N)�__name__� __module__� __qualname__�__doc__r? r rM rP rV r= rm rh ri rj r% r% r% r( r! 5 s ,$ Q%r! c s� t � �dkr| S | j�� �jkr&� g� �fdd�� D �� � fdd�t�j�D �}�fdd�� D �}�fdd�� D �}�fdd�� D �}�fdd�|D �}�fd d�|D �}�fd d�|D �} td d � |D ��} t|| d d d�} t| d d�\} } t| | | |d d�}|�st | dd�}n"t || g || g | dg d d�}t | t ��rl| � � }||_|jd|d�}|}|}|}n�t | jt ��r�| }x>tt � ��D ].}� | �|j�|d�}�fdd�� D �� �q�W |S | j d d�}||_|jd|d�}t |t ��r�|j}n|j}t |t ��s t�|jd g| }| jjg| }|jd g}x$|D ] }|�|�|jd �� �q<W t |||d d�}t |t ��r�||_n||_|S )Nr c s g | ]}� � |��qS r% )r, )r' �i)r# r% r( r) d s z%_unstack_multiple.<locals>.<listcomp>c s g | ]}|� kr|�qS r% r% )r' r� )�clocsr% r( r) f s c s g | ]}� j | �qS r% )r1 )r' r� )r# r% r( r) h s c s g | ]}� j | �qS r% )r. )r' r� )r# r% r( r) i s c s g | ]}� j | �qS r% )r3 )r' r� )r# r% r( r) j s c s g | ]}� j | �qS r% )r1 )r' r� )r# r% r( r) k s c s g | ]}� j | �qS r% )r. )r' r� )r# r% r( r) l s c s g | ]}� j | �qS r% )r3 )r' r� )r# r% r( r) m s c s s | ]}t |�V qd S )N)rA )r' rB r% r% r( rC o s z$_unstack_multiple.<locals>.<genexpr>F)�sort�xnull)r� )r� �__placeholder__)rw )r1 r. r3 r� )rl c s g | ]}|� k r|n|d �qS )r$ r% )r' rE )�valr% r( r) � s )�deepr" )rA r# r3 �range�nlevelsrD r r r r r rx r rq r~ rd �AssertionErrorr1 rw r. r� rN )�datar� rl ZrlocsZclevelsZccodesZcnamesZrlevelsZrcodesZrnamesrg r] Zcomp_idsrJ Z recons_codesZ dummy_index�dummyZ unstackedra r� r� �resultr� ZunstcolsZrec� new_columnsr% )r� r# r� r( �_unstack_multipleX sp r� c C s� t |ttf�r0t|�dkr(t| ||d�S |d }t|�sL|dksL| j�|�}t | t�r�t | jt �rpt | ||d�S | j j dd�S n^t | jt �s�t dt| j�� d���n:t| j�r�t| ||�S t| j|| jd �}|j| jd |d �S d S ) Nr$ )rl r r� F)�dropnaz'index must be a MultiIndex to unstack, z was passed)r- r+ )rk rl )rx rD r0 rA r� r r# r, r r �_unstack_frame�T�stackr< �typer r* �_unstack_extension_seriesr! Z_constructor_expanddimrm �_values)�objr- rl � unstackerr% r% r( r~ � s& r~ c C sX | j s.t| j|d�}| jj||d�}| �|�S t| j|| jd�}|j| j| j|d�S d S )N)r- )rl )r- r+ )rk rl ) Z_can_fast_transposer! r# Z_mgrr~ � _constructorrm r� rd )r� r- rl r� Zmgrr% r% r( r� � s r� c C s$ | � � }|j||d�}|jddd�S )a' Unstack an ExtensionArray-backed Series. The ExtensionDtype is preserved. Parameters ---------- series : Series A Series with an ExtensionArray for values level : Any The level name or number. fill_value : Any The user-level (not physical storage) fill value to use for missing values introduced by the reshape. Passed to ``series.values.take``. Returns ------- DataFrame Each column of the DataFrame will have the same dtype as the input Series. )r- rl r r$ )r- rR )Zto_framer~ Z droplevel)Zseriesr- rl Zdfr� r% r% r( r� � s r� r" Tc s� dd� }| j \}� | j�|�}t| jt�r8t| ||d�S t| jt�r�t| jj�}� fdd�| jj D �}|| j�\}} |� |� |� t � | |�� � � t| jj�} | � | jj� t||| dd�} nTtt|| j| jf�� \} \} } | �� �t � | |�� � f}t| || jj| jjgdd�} | j�s|| j�r|t| jj�}|d }t|��rp|�� }|�d d� | �� D ��}t||� �}n | j� � }n | j� � }|�r�t |�}|| }| | } | j || d �S ) z� Convert DataFrame to Series with multi-level Index. Columns become the second level of the resulting hierarchical index Returns ------- stacked : Series c S s, | j r| t�t| ��fS t| �\}}||fS )N)Z is_uniquer9 r_ rA r )r# r. � categoriesr% r% r( � factorize s zstack.<locals>.factorize)� level_numr� c s g | ]}|� � ��qS r% )r� )r' r� )�Kr% r( r) s zstack.<locals>.<listcomp>F)r1 r. r3 r� r c S s g | ]\}}|j �qS r% )r� )r' rT �colr% r% r( r) 4 s )r# ) rg rd r, rx r �_stack_multi_columnsr# r0 r1 r. r� r9 r� �ravelr3 rw �zip�mapr� rv �_is_homogeneous_type�dtypesr� r ry �_concat_same_type�items�"_reorder_for_extension_array_stackr Z_constructor_sliced)�framer- r� r� �Nr� ra r� ZclevZclabr� rj r1 Zilabr. r� r* �arrr� r^ r% )r� r( r� � sJ r� c s� t � fdd�|D ��r8� }x�|D ]}t|||d�}q W n�t dd� |D ��rƈ }� fdd�|D �}xltt|��D ]R}|| }t|||d�}g }x.|D ]&}||kr�|�|d � q�|�|� q�W |}qnW ntd��|S ) Nc 3 s | ]}|� j jkV qd S )N)rd r3 )r' r� )r� r% r( rC J s z!stack_multiple.<locals>.<genexpr>)r� c s s | ]}t |t�V qd S )N)rx �int)r' r� r% r% r( rC P s c s g | ]}� j �|��qS r% )rd r, )r' r� )r� r% r( r) V s z"stack_multiple.<locals>.<listcomp>r$ zTlevel should contain all level names or all level numbers, not a mixture of the two.)rs r� r� rA r� r< )r� r- r� r� r� r# Z updated_level�otherr% )r� r( �stack_multipleG s& r� r )rd r@ c C s� t | j�dkr&| jd j| jd d�S dd� t| jdd� | jdd� �D �}t|� }dd � t�|�D �}t|� }tj d d� t|| j�D �| jdd� d �S ) zBCreates a MultiIndex from the first N-1 levels of this MultiIndex.rn r )rw c s" g | ]\� }� fd d�|D ��qS )c s g | ]}|d kr� | nd�qS )r Nr% )r' �c)r� r% r( r) v s z8_stack_multi_column_index.<locals>.<listcomp>.<listcomp>r% )r' r. r% )r� r( r) v s z-_stack_multi_column_index.<locals>.<listcomp>Nr" c s s | ]\}}|V qd S )Nr% )r' �keyrT r% r% r( rC | s z,_stack_multi_column_index.<locals>.<genexpr>c S s* g | ]"\}}d |kr"t ||jd�n|�qS )N)r* )r r* )r' Znew_levr� r% r% r( r) � s )r3 ) rA r1 r� r3 r� r. � itertools�groupbyr Z from_arrays)rd rF ZtuplesZ unique_tuplesZnew_levsr% r% r( �_stack_multi_column_indexp s "r� c " sV dd� }| � � }|| jjd krl|j}x>t|| jjd �D ](}|||�}||d |�}|�||�}q:W ||_|j�� s�|d|j�} |j| dd�}t|j�} i } |jjd } t t |jj d ��} | � t | �d �}t�|| �}t | �� g }�x~| D �]t}y|j�|�}W n" tk �r& |�|� w�Y nX t|t��s>t |�}n |j|j }|� k�r�|jd d �|j| f }|�|jj d �|_|j|d�j}n�| j�r t| j j d ��r ||j| j j d }||j| }|� � � dd � |� � D ��}|j!\}}t�"|| ��#||�j$�%� }|�|�}n,| j&�r6||j| j}n|jd d �|f }|j'dk�r\|�%� }|| |< q�W t |�dk�r�| �(|�} t |�}t|j)t*��r�t+|j)j�}t+|j)j,�} � fd d �|j)j D �} n*t-|j)�\} } | g}| �.� �g} |j)j/g} |�| � | �t�0| |�� | �| jj,| � t*|| | d d �} | j1| | | d �}!|�rR|!j2ddd�}!|!S )Nc S s | |j kr|j | S | S )z� Logic for converting the level number to something we can safely pass to swaplevel. If `level_num` matches a column name return the name from position `level_num`, otherwise return `level_num`. )r3 )r� rd r% r% r( �_convert_level_number� s z3_stack_multi_columns.<locals>._convert_level_numberr$ r )r- rR r" )rd c S s g | ]\}}|j �qS r% )r� )r' rT rB r% r% r( r) � s z(_stack_multi_columns.<locals>.<listcomp>c s g | ]}|� � ��qS r% )r� )r' r� )�levsizer% r( r) � s F)r1 r. r3 r� )r# rd rs )rR �how)3rq rd r� r� Z swaplevelZ _is_lexsortedZ sort_indexr� r1 �sorted�setr. r� rA r9 rN Zget_loc�KeyErrorr� rx �slice�stop�start�locZreindexrQ r� r r� Zilocry r� r� rg r_ rt r� r� Z_is_mixed_typere � differencer# r r0 r3 r r� rw r� r� r� )"r� r� r� r� �thisZ roll_columnsr� Zlev1Zlev2Z level_to_sortr� Znew_dataZ level_valsr� Zlevel_vals_nanZlevel_vals_usedZ drop_colsr� r� Z slice_len�chunkZ value_slicer* Zsubsetr� r� �idxra r� r� Z old_codesZ old_levelsrj r� r% )r� r( r� � s� r� rT FrZ z Dtype | Noner )�dummy_na�sparse� drop_firstr* r@ c s� ddl m} ddg} t| t��rx|dkr6| j| d�� nt|�sHtd��n| | � � fdd �} | �d � | �d � t�t�r�t� �g��t�t �r��fd d �� j D ���dkr�� j �t�t�r�t� �g��n t�t �r�fdd �� j D ��� j | j kr�g } n*|dk �r| j |dd�g} n| j| d�g} xDt� �� ���D ]0\} } }t| d | |||||d�}| �|� �q6W || dd�}nt| ��||||d�}|S )ay Convert categorical variable into dummy/indicator variables. Parameters ---------- data : array-like, Series, or DataFrame Data of which to get dummy indicators. prefix : str, list of str, or dict of str, default None String to append DataFrame column names. Pass a list with length equal to the number of columns when calling get_dummies on a DataFrame. Alternatively, `prefix` can be a dictionary mapping column names to prefixes. prefix_sep : str, default '_' If appending prefix, separator/delimiter to use. Or pass a list or dictionary as with `prefix`. dummy_na : bool, default False Add a column to indicate NaNs, if False NaNs are ignored. columns : list-like, default None Column names in the DataFrame to be encoded. If `columns` is None then all the columns with `object` or `category` dtype will be converted. sparse : bool, default False Whether the dummy-encoded columns should be backed by a :class:`SparseArray` (True) or a regular NumPy array (False). drop_first : bool, default False Whether to get k-1 dummies out of k categorical levels by removing the first level. dtype : dtype, default np.uint8 Data type for new columns. Only a single dtype is allowed. Returns ------- DataFrame Dummy-coded data. See Also -------- Series.str.get_dummies : Convert Series to dummy codes. Examples -------- >>> s = pd.Series(list('abca')) >>> pd.get_dummies(s) a b c 0 1 0 0 1 0 1 0 2 0 0 1 3 1 0 0 >>> s1 = ['a', 'b', np.nan] >>> pd.get_dummies(s1) a b 0 1 0 1 0 1 2 0 0 >>> pd.get_dummies(s1, dummy_na=True) a b NaN 0 1 0 0 1 0 1 0 2 0 0 1 >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'], ... 'C': [1, 2, 3]}) >>> pd.get_dummies(df, prefix=['col1', 'col2']) C col1_a col1_b col2_a col2_b col2_c 0 1 1 0 0 1 0 1 2 0 1 1 0 0 2 3 1 0 0 0 1 >>> pd.get_dummies(pd.Series(list('abcaa'))) a b c 0 1 0 0 1 0 1 0 2 0 0 1 3 1 0 0 4 1 0 0 >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True) b c 0 0 0 1 1 0 2 0 1 3 0 0 4 0 0 >>> pd.get_dummies(pd.Series(list('abc')), dtype=float) a b c 0 1.0 0.0 0.0 1 0.0 1.0 0.0 2 0.0 0.0 1.0 r )�concatrp �categoryN)�includez1Input must be a list-like for parameter `columns`c sH t | �rDt| �� jd ksDd|� dt| �� d� jd � d�}t|��d S )Nr$ z Length of 'z' (z9) did not match the length of the columns being encoded (z).)r rA rg r< )r� rw Zlen_msg)�data_to_encoder% r( � check_len} s "z get_dummies.<locals>.check_len�prefix� prefix_sepc s g | ] }� | �qS r% r% )r' r� )r� r% r( r) � s z get_dummies.<locals>.<listcomp>c s g | ] }� | �qS r% r% )r' r� )r� r% r( r) � s r$ )rR )�exclude)r� r� r� r� r� r* )r� r� r* )�pandas.core.reshape.concatr� rx r Z select_dtypesr � TypeError�strr� �cycle�dictrd rg Zdropr� r� �_get_dummies_1dr� )r� r� r� r� rd r� r� r* r� Zdtypes_to_encoder� Z with_dummiesr� �pre�sepr� r� r% )r� r� r� r( � get_dummies s\ i r� c sr ddl m} tt| ��\}} |d kr*tj}t�|�}t|�rDtd��dd�dd�} |sjt | �dkrj| | �S |� � }|r�t | �||dk< t� | tj �} |r�t | �d kr�| | �S t | �} � d kr�| } nt � �fd d �| D ��} t| t�r�| j} nd } |�rt|��rd}n|tk�rd }nd }g }t | �}dd � tt | ��D �}|dk}|| }t�|�| }x&t||�D ]\}}|| � |� �qfW |�r�|d d � }| d d � } xPt| |�D ]B\}}ttjt |�|d�t||�||d�}|� t|| |d�� �q�W ||d d d�}tt|�}|S tj| |d�j|d d�j }|�s:d||dk< |�r`|d d �d d �f }| d d � } t|| | d�S d S )Nr )r� z1dtype=object is not a valid dtype for get_dummiesr )r@ c S s* t | t�r| j}nt�t| ��}t|d�S )N)r# )rx r r# r9 r_ rA r )r� r# r% r% r( �get_empty_frame� s z(_get_dummies_1d.<locals>.get_empty_framer" r$ c s g | ]}� � �� |� ��qS r% r% )r' r- )r� r� r% r( r) � s z#_get_dummies_1d.<locals>.<listcomp>Fg c S s g | ]}g �qS r% r% )r' rT r% r% r( r) s )r* )Z sparse_indexrl r* )r� r# rw )rR rq )rR )r# rd ) r� r� r r r9 Zuint8r* r r< rA rq r� �nanr rx r# r rZ r� r_ r� r ru r r r �eyerN r� )r� r� r� r� r� r� r* r� r. r1 r� Znumber_of_colsZ dummy_colsr# rl Z sparse_seriesr� Z sp_indicesr^ Zn_idxZndx�coder� ZixsZsarr�outZ dummy_matr% )r� r� r( r� � sr r� r r� )r� �n_rows� n_columnsr@ c C s& t �|| ��||�j�� }| �|�S )a� Re-orders the values when stacking multiple extension-arrays. The indirect stacking method used for EAs requires a followup take to get the order correct. Parameters ---------- arr : ExtensionArray n_rows, n_columns : int The number of rows and columns in the original DataFrame. Returns ------- taken : ExtensionArray The original `arr` with elements re-ordered appropriately Examples -------- >>> arr = np.array(['a', 'b', 'c', 'd', 'e', 'f']) >>> _reorder_for_extension_array_stack(arr, 2, 3) array(['a', 'c', 'e', 'b', 'd', 'f'], dtype='<U1') >>> _reorder_for_extension_array_stack(arr, 3, 2) array(['a', 'd', 'b', 'e', 'c', 'f'], dtype='<U1') )r9 r_ rt r� r� rN )r� r� r� r� r% r% r( r� 0 s ! r� )N)N)N)r" T)T)r" T)NrT FNFFN)rT FFFN)F� __future__r r� �typingr r �numpyr9 Zpandas._libs.reshapeZ_libsrt r} Zpandas._libs.sparser Zpandas._typingr Zpandas.util._decoratorsr Zpandas.core.dtypes.castr Zpandas.core.dtypes.commonr r r r r r r r r Zpandas.core.dtypes.dtypesr Zpandas.core.dtypes.missingr Zpandas.core.algorithms�coreZ algorithmsrS Zpandas.core.arraysr Z pandas.core.arrays.categoricalr Zpandas.core.constructionr Zpandas.core.framer Zpandas.core.indexes.apir r Zpandas.core.seriesr Zpandas.core.sortingr r r r r r r! r� r~ r� r� r� r� r� r� r� r� r� r% r% r% r( �<module> s\ , % W % J ) } 6 i
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